PATENT DOCUMENT

Publication Number: US-11710035-B2
Application Number: US-201916556066-A
Country: US
Kind Code: B2

Title: Distributed labeling for supervised learning

Abstract:
Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

Claims:
What is claimed is: 
     
       1. A data processing system comprising:
 a memory device to store instructions; 
 one or more processors to execute the instructions stored on the memory device, the instructions to cause the data processing system to perform operations comprising:
 sending a set of unlabeled data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the set of unlabeled data, wherein each of the mobile electronic devices include a first machine learning model; 
 receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors to the set of proposed labels; 
 processing the set of proposed labels to determine an estimate of a most frequently proposed label for an element of unlabeled data; 
 adding the element of unlabeled data and a corresponding most frequently proposed label to a training data set; and 
 training a second machine learning model using the training data set, the second machine learning model on a server device. 
 
 
     
     
       2. The data processing system as in  claim 1 , wherein the unlabeled set of data includes one or more of text data, image data, application activity data, and device activity data. 
     
     
       3. The data processing system as in  claim 1 , wherein the first machine learning model is configured as a convolutional neural network. 
     
     
       4. The data processing system as in  claim 1 , wherein the first machine learning model is configured as a recurrent neural network. 
     
     
       5. The data processing system as in  claim 1 , wherein the second machine learning model is configured as a convolutional neural network. 
     
     
       6. The data processing system as in  claim 1 , wherein the second machine learning model is configured as a recurrent neural network. 
     
     
       7. The data processing system as in  claim 1 , the set of proposed labels encoded to mask individual contributors via a privacy-preserving encoding algorithm. 
     
     
       8. The data processing system as in  claim 7 , wherein the privacy-preserving encoding algorithm is a differential privacy algorithm. 
     
     
       9. The data processing system as in  claim 8 , wherein processing the set of proposed labels to determine an estimate of a most frequent proposed label for the unlabeled set of data includes generating a sketch matrix of received proposed labels to aggregate proposed label data. 
     
     
       10. The data processing system as in  claim 8 , wherein processing the set of proposed labels to determine an estimate of a most frequent proposed label for the unlabeled set of data includes generating a histogram of received proposed labels to aggregate proposed label data. 
     
     
       11. A data processing system on a mobile electronic device, the data processing system comprising:
 a memory device to store instructions; 
 one or more processors to execute the instructions stored on the memory device, the instructions to cause the one or more processors to:
 select a set of data on the mobile electronic device; 
 generate a training set based on selected data; 
 train a first machine learning model using the training set; 
 receive an unlabeled set of data from a server; 
 generate proposed labels for elements of the unlabeled set of data; and 
 transmit a privatized version of a proposed label to the server. 
 
 
     
     
       12. The data processing system as in  claim 11 , wherein the first machine learning model is configured as a convolutional neural network or a recurrent neural network. 
     
     
       13. The data processing system as in  claim 12 , wherein the server is to train a second machine learning model based on the privatized version of a proposed label. 
     
     
       14. The data processing system as in  claim 13 , wherein the first machine learning model is a different type of machine learning model than the second machine learning model. 
     
     
       15. The data processing system as in  claim 11 , the one or more processors to generate the privatized version of a proposed label via a privacy-preserving encoding. 
     
     
       16. The data processing system as in  claim 15 , wherein the privacy-preserving encoding is based on a differential privacy algorithm. 
     
     
       17. The data processing system as in  claim 11 , wherein to select the set of data includes to determine types of data on the mobile electronic device having a number of elements sufficient to train the first machine learning model to a specified minimum accuracy. 
     
     
       18. The data processing system as in  claim 17 , wherein a selected set of data includes text data, image data, application activity data, or device activity data. 
     
     
       19. The data processing system as in  claim 18 , wherein to receive an unlabeled set of data from a server includes to receive a type of data upon which the first machine learning model is trained. 
     
     
       20. The data processing system as in  claim 19 , wherein the mobile electronic device is grouped with other mobile electronic devices based on the type of data upon which machine learning models of the other mobile electronic devices are trained. 
     
     
       21. A method comprising:
 selecting, by an electronic device, a set of data on the electronic device; 
 generating, by the electronic device, a training set based on the selected data; 
 training, by the electronic device, a machine learning model using the training set; 
 receiving, by the electronic device, an unlabeled set of data from a server; 
 generating, by the electronic device, proposed labels for elements of the unlabeled set of data; and 
 transmitting, by the electronic device, a privatized version of one of the proposed labels to the server. 
 
     
     
       22. The method of  claim 21 , further comprising:
 generating, by the electronic device, the privatized version of the one of the proposed labels via a privacy-preserving encoding. 
 
     
     
       23. A non-transitory machine-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
 selecting, by an electronic device, a set of data on the electronic device; 
 generating, by the electronic device, a training set based on the selected data; 
 training, by the electronic device, a machine learning model using the training set; 
 receiving, by the electronic device, an unlabeled set of data from a server; 
 generating, by the electronic device, proposed labels for elements of the unlabeled set of data; and 
 transmitting, by the electronic device, a privatized version of one of the proposed labels to the server. 
 
     
     
       24. The non-transitory machine-readable medium of  claim 23 , wherein the operations further comprise:
 generating, by the electronic device, the privatized version of the one of the proposed labels via a privacy-preserving encoding.

Description:
CROSS-REFERENCE 
     This application claims benefit of U.S. Provisional Patent Application No. 62/738,990 filed Sep. 28, 2018, which is hereby incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to the field of machine learning via privatized data. More specifically, this disclosure relates to a system that implements one or more mechanisms to enable privatized distributed labeling for supervised training of machine learning models. 
     BACKGROUND 
     Machine learning is an application of artificial intelligence that enables a complex system to automatically learn and improve from experience without being explicitly programmed. The accuracy and effectiveness of machine learning models can depend in part on the data used to train those models. For example, machine learning classifiers can be trained using a labeled data set, in which samples of data that the classifier is to learn to recognize are provided to the classifier along with one or more labels that identify a classification for the sample. Generally, a larger training dataset results in a more accurate classifier. However, current techniques used to prepare training datasets may be painstaking, time consuming, and expensive, particularly techniques that involve the manual labeling of data to generate the training dataset. 
     SUMMARY 
     Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. 
     One embodiment provides for a data processing system comprising a memory device to store instructions and one or more processors to execute the instructions stored on the memory device. The instructions cause the data processing system to perform operations comprising sending an unlabeled set of data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the unlabeled set of data, wherein each of the mobile electronic devices include a variant of a first machine learning model; receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors of each proposed label in the set of proposed labels; processing the set of proposed labels to determine a most frequent proposed label for the unlabeled set of data; adding the unlabeled set of data and the most frequent proposed label to a first training set; and training a second machine learning model using the first training set, the second machine learning model on a server device. 
     One embodiment provides for a non-transitory machine readable medium storing instructions to cause one or more processors to perform operations comprising sending an unlabeled set of data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the unlabeled set of data, wherein each of the mobile electronic devices include a first machine learning model; receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors to the set of proposed labels; processing the set of proposed labels to determine an estimate of a most frequent proposed label for the unlabeled set of data; adding the unlabeled set of data and corresponding most frequent proposed labels to a first training set; and training a second machine learning model using the first training set, the second machine learning model on a server device. 
     One embodiment provides for a data processing system on a mobile electronic device, the data processing system comprising a memory device to store instructions and one or more processors to execute the instructions stored on the memory device. The instructions cause the one or more processors to select a set of data on the mobile electronic device; generate a training set based on selected data; train a first machine learning model using the training set; receive an unlabeled set of data from a server; generate proposed labels for elements of the unlabeled set of data; and transmit a privatized version of one or more proposed labels to the server. 
     One embodiment provides for a non-transitory machine readable medium storing instructions to cause one or more processors to perform operations comprising selecting a set of data on a mobile electronic device; generating a training set based on selected data; training a first machine learning model using the training set, the first machine learning model trained on the mobile electronic device; receiving an unlabeled set of data from a server; generating proposed labels for elements of the unlabeled set of data; and transmitting a privatized version of a proposed label to the server. 
     Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description, which follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements. 
         FIG.  1    illustrates a system to enable crowdsourced labeling of training data for a machine learning model according to embodiments described herein. 
         FIG.  2    illustrates a system for receiving privatized crowdsourced labels from multiple client devices, according to an embodiment. 
         FIG.  3 A  is a block diagram of a system for generating privatizing proposed labels for server provided unlabeled data, according to an embodiment. 
         FIG.  3 B  is a diagram of data flow for system, according to an embodiment. 
         FIG.  4 A  is a flow diagram of a method to improve the accuracy of a machine learning model via crowdsourced labeling of unlabeled data, according to an embodiment. 
         FIG.  4 B  is a flow diagram of a method to generate a privatized proposed label on a client device, according to an embodiment. 
         FIGS.  5 A- 5 C  illustrate exemplary privatized data encodings that can be used in embodiments described herein that implement privatization via differential privacy. 
         FIGS.  6 A- 6 B  are example processes for encoding and differentially privatizing proposed labels to be transmitted to a server, according to embodiments described herein. 
         FIGS.  7 A- 7 D  are block diagrams of multibit histogram and count-mean-sketch models of client and server algorithms according to an embodiment. 
         FIG.  8    illustrates data that can be labeled in a privatized manner, according to embodiments. 
         FIG.  9 A  illustrates device activity sequences that can be learned in a privatized manner, according to an embodiment. 
         FIG.  9 B  illustrates exemplary device activity that can be used to train a predictor model on a client device. 
         FIG.  10    illustrates compute architecture on a client device that can be used to enable on-device, semi-supervised training and inferencing using machine learning algorithms, according to embodiments described herein. 
         FIG.  11    is a block diagram of mobile device architecture, according to an embodiment. 
         FIG.  12    is a block diagram illustrating an example computing system that can be used in conjunction with one or more of the embodiments of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments and aspects will be described herein with reference to details discussed below. The accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of various embodiments. However, in certain instances, well-known or conventional details are not described to provide a concise discussion of embodiments. 
     Reference in the specification to “one embodiment” or “an embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment. The appearances of the phrase “embodiment” in various places in the specification do not necessarily all refer to the same embodiment. It should be noted that there could be variations to the flow diagrams or the operations described therein without departing from the embodiments described herein. For instance, operations can be performed in parallel, simultaneously, or in a different order than illustrated. 
     A key roadblock in the implementation of many supervised learning techniques is the requirement to have labeled data on the training server. Existing solutions to the labeled data problem include centralizing the training data and manually annotating the data with one or more labels. Where the training data is user data, maintaining such data on a server can risk a loss of user privacy. Additionally, manually labeling the training data may be cost prohibitive. 
       FIG.  1    illustrates a system  100  to enable crowdsourced labeling of training data for a machine learning model according to embodiments described herein. As shown in  FIG.  1   , in one embodiment, a server  130  can connect with a set of client devices  110   a - 110   n ,  111   a - 111   n ,  112   a - 112   n  over a network  120 . The server  130  can be any kind of server, including an individual server or a cluster of servers. The server  130  can also be or include a cloud-based server, application server, backend server, virtual server, or combination thereof. The network  120  can be any suitable type of wired or wireless network such as a local area network (LAN), a wide area network (WAN), or combination thereof. Each of the client devices can include any type of computing device such as a desktop computer, a tablet computer, a smartphone, a television set top box, or other computing device. For example, a client device can be an iPhone®, Apple® Watch, Apple® TV, etc., and can be associated with a user within a large set of users to which tasks can be crowdsourced with the permission of the user. 
     In one embodiment, the server  130  stores a machine learning module  135 , which can include a machine learning model implemented using on a neural network, such as but not limited to a deep learning neural network. For example, the machine learning module  135  can include a convolutional neural network (CNN) or a recurrent neural network (RNN), including a long short-term memory (LSTM) variant of an RNN. Other types of machine learning models and/or neural networks can be used. The machine learning module  135  can include an implementation of a basic, low accuracy learning model that untrained or pre-trained using generic data. The server  130  can also store a set of unlabeled data  131 . In one embodiment, the unlabeled data  131  is a large set of data that will be labeled and used to increase the accuracy of the machine learning module  135 . 
     The unlabeled data  131  includes several types of data, including the types of data for which the machine learning module  135  can be configured to classify. However, the system  100  is not limited for use with any particular type of data and can be configured based on the type of data to be learned or classified. For example, the system  100  can be used for image data, but is not limited to any specific type of data. For example, image data can be used for an image-based classification model, such as an image classifier, which can be configured for object detection or facial recognition. The system  100  can also be configured to train a predictive system. A sequence of characters and words can be used to train a predictive model for a predictive keyboard. For example, the machine learning module  135  can be trained such that, for a given set of input characters, a next character or word can be predicted. A sequence of applications can be used to train an application predictor. For example, for a given sequence of applications accessed or used by a user, the machine learning module  135  can be trained to predict the next application or applications that are likely to be accessed by a user and present icons for those applications in an area of a user interface that is easily and readily accessible to the user. In one embodiment, a mapping application can use a variant of the machine learning module  135  to predict a navigation destination for a user based on a reset of recent locations or destinations for a user. In one embodiment a combination of device and application activity can be used to train the machine learning module  135  to predict upcoming user activities with a device. For example, when a mobile device connects to an automobile&#39;s Bluetooth audio, or other in-vehicle infotainment device, on a weekday morning, the machine learning module  135  can be used to predict that the user is preparing to commute to a work destination. In one embodiment, data associated with virtual assistant shortcuts can also be used to train the machine learning model  135 . Virtual assistant shortcuts can be used to automate one or more tasks across multiple applications of the device. 
     The client devices can be organized into device groups (e.g., device group  110 , device group  111 , device group  112 ) that can each contain multiple client devices. Each device group can contain n devices, where n can be any number of devices. For example, device group  110  can contain client device  110   a - 110   n . Device group  111  can contain client device  111   a - 111   n . Device group  112  can contain client device  112   a - 112   n . In one embodiment, each device group can contain up to 128 devices, although the number of client devices in each device group can vary across embodiments and is not limited to any specific number of devices. In general, a large number of devices are used per group to enable the labeling system to be resilient against dropouts by clients within a group, such that the system does not require all devices within a group to provide a proposed label. The number of devices in each device group can be the same for each group or can vary across groups. In one embodiment, the server  130  may require a threshold number of devices within each group to send a proposed label before a specific one of the proposed labels is selected. 
     In one embodiment, each client device (client device  110   a - 110   n , client device  111   a - 111   n , client device  112   a - 112   n ) can include a local machine learning module. For example, client device  110   a - 110   n  of device group  110  can each contain corresponding local machine learning module  136   a - 136   n . Client device  111   a - 111   n  of device group  111  can each contain corresponding local machine learning module  137   a - 137   n . Client device  112   a - 112   n  of device group  112  can each contain a corresponding local machine learning module  138   a - 138   n . In various embodiments, the local machine learning modules can be loaded on each client device during factory provisioning or can be loaded or updated when a system image of the client device is updated. In one embodiment, each local machine learning module can initially be a variant of the machine learning module  135  of the server. However, the local machine learning modules can include different types of learning models than the learning model used by the server. In one embodiment, the local machine learning modules  136   a - 136   n ,  137   a - 137   n ,  138   a - 138   n  on each client device can include LSTM networks, while the machine learning module  135  on the server  130  may be a CNN. The local machine learning models on the client devices be individualized to each client device by training on local data stored on the client device. 
     In one embodiment, devices are grouped based on the type of data upon which their respective machine learning models will be trained. For example, the local machine learning modules  136   a - 136   n  can be trained on text data stored on client devices  110   a - 110   n , while machine learning modules  137   a - 137   n  can be trained on image data stored on client devices  111   a - 111   n . Local machine learning modules  138   a - 138   n  can be trained on application or device activity data associated with client devices  112   a - 112   n . In one embodiment the server  130  and the client devices can synchronize on the types of available data to be used to training and the devices can be grouped accordingly. 
     The server can provide a set of unlabeled data (e.g., a set of unlabeled data  121 , a set of unlabeled data  122 , a set of unlabeled data  123 ) to each client device within each device group. The sets of unlabeled data can each include one or more units of unlabeled data  131 [ i ] for which the client devices can generate proposed labels based on the individualized machine learning modules  136   a - 136   n ,  137   a - 137   n ,  138   a - 138   n  on each client device. In one embodiment, the set of unlabeled data transmitted to devices in a device group includes the same unit or units of unlabeled data, with each device group receiving a different unit of unlabeled data. For example, the set of unlabeled data  121  provided to each client device  110   a - 110   n  in device group  110  can include a first unit of unlabeled data. The set of unlabeled data  122  provided to each client device  111   a - 111   n  in device group  111  can include a second unit of unlabeled data. The unlabeled data  123  provided to each client device  112   a - 112   n  in device group  112  can include a third unit of unlabeled data. 
       FIG.  2    illustrates a system  200  for receiving privatized crowdsourced labels from multiple client devices, according to an embodiment. In one embodiment, the system  200  includes a set of client devices  210   a - 210   c  (collectively,  210 ), which can be any of the client devices described above (e.g., client devices  110   a - 110   n ,  111   a - 111   n ,  112   a - 112   n ). The client devices  210 , using the techniques described above, can each generate privatized proposed labels  212   a - 212   c  (privatized proposed label  212   a  from client device  210   a , privatized proposed label  212   b  from client device  210   b , privatized proposed label  212   c  from client device  210   c ) which each can be transmitted to the server  130  via the network  120 . In one embodiment, the privatized proposed labels  212   a - 212   c  are sent as a tuple that includes a proposed label and the element of the set of unlabeled data to which the proposed label corresponds. In one embodiment the transmitted tuple includes the proposed label and an identifier of the associated element of the set of unlabeled data. In one embodiment, multiple tuples of proposed labels and associated elements can be transmitted from one or more of the client devices  210  depending on the privacy budget available to the transmitting device. 
     The illustrated client devices  210  can be in the same device group or different device groups. For example, client device  210   a  can represent client device  110   a  of device group  110  in  FIG.  1   , while client device  210   b  can represent client device  111   a  of device group  111  in  FIG.  1   . Where the client devices  210  are in different device groups, the privatized proposed labels  212   a - 212   c  can each correspond with a different unit or units of unlabeled data provided by the server  130 . For example, client device  210   a  can receive at least a first unit of unlabeled data, which can differ from a second unit of unlabeled data received by client device  210   b . Where the client devices  210  are in the same device group, the privatized proposed labels  212   a - 212   c  can correspond with the same unit or units of unlabeled data provided by the server (e.g., unlabeled data  131 [ i ] in the set of unlabeled data  121  shown in  FIG.  1   ). Although the proposed labels are for the same unit of data, the labels proposed by the client devices  210  can differ, as the labels are proposed based on individualized machine learning models on each client device, where the individualized machine learning models are individualized based on the local data stored in each client device  210   a - 210   c.    
     Prior to transmission to the server  130  over the network  120 , the proposed labels generated on the client devices  210  are privatized to generate the privatized proposed labels  212   a - 212   c . The privatization is performed to mask the identity of the contributor of any proposed label in the crowdsourced dataset and can be performed using one or more data privatization algorithms or techniques. Some embodiments described herein apply a differential privacy encoding to the proposed labels, while other embodiments can implement homomorphic encryption, secure multiparty compute, or other privatization techniques. 
     The server  130  maintains data store of proposed label aggregate data  230 , which is an aggregation of the privatized proposed labels  212   a - 212   c  received from the client devices  210 . The format of the proposed label aggregate data  230  can vary based on the privatization technique applied to the proposed labels. In one embodiment, a multibit histogram differential privacy technique is used to privatize the proposed labels and the proposed label aggregate data  230  is a histogram containing proposed label frequency estimates. The server can process the proposed label aggregate data  230  to determine a most frequently proposed label for each unit of unlabeled data  131  and label each unit, generating a set of crowdsourced labeled data  231 . The crowdsourced labeled data  231  can then be used to train and enhance machine learning models. 
       FIG.  3 A  is a block diagram of a system  300  for generating privatizing proposed labels for server provided unlabeled data, according to an embodiment. The system  300  includes a client device, which can be any of client devices  110   a - 110   n ,  111   a - 111   n ,  112   a - 112   n  or client devices  210 . The client device  310  includes a machine learning module  361  to perform supervised learning, having a learning model that has been trained using client data  332  on the client device  310 . The trained machine learning module  361  can then be used to generate a proposed label  333  for one or more elements of unlabeled data  131  received from the server  130 . In one embodiment, the client device  310  can have a privacy engine  353  that includes a privacy daemon  356  and a privacy framework or application programming interface (API)  355 . The privacy engine  353  can use various tools, such as hash functions, including cryptographic hash functions, to privatize a proposed label  333  generated by the client device  310 . In one embodiment the privacy engine  353  can privatize the proposed label  333  using one or more of a variety of privatization techniques including, but not limited to differential privacy algorithms. The privatized proposed label  333  can then be transmitted to the server  130  via the network  120 . 
     The server  130  can include a receive module  351  and a frequency estimation module  341  to determine label frequency estimations  331 , which can be stored in various data structures, such as an array as in the multibit histogram algorithm. The receive module  351  can asynchronously receive crowdsourced privatized labels of from a large plurality of client devices. In one embodiment, the receive module  351  can remove latent identifiers from the received data. Latent identifiers can include IP addresses, metadata, session identifiers, or other data that might identify the client device  310 . The frequency estimation module  341  can also process received privatized proposed labels using operations such as, but not limited to a count-mean-sketch or multi-bit histogram operations. The label frequency estimations  331  can be analyzed by a labeling and training module  330 , which can determine labels for unlabeled server data by applying to each unit of unlabeled data, for example, the highest frequency label received for the unit of unlabeled server data, although other methods of determining labels can be used. The labeling and training module  330  can use the determined labels to train an existing server-side machine learning module  135  into an improved server-side machine learning module  346 . In one embodiment, the client device  310  and the server  130  can engage in an iterative process to enhance the accuracy of a machine learning model implemented by the machine learning module. In one embodiment the improved machine learning module  346  can be deployed to the client device  310  via a deployment module  352  if the machine learning module  361  on the client device  310  is compatible with the improved machine learning module  346 . Alternatively, a version of the machine learning models used by the client device  310  can be enhanced or updated on the server  130  and deployed to the client device  310  via the deployment module  352 , for example if the machine learning module  361  implements a different type of model as the improved machine learning module  346  on the server  130 . In one embodiment the deployment module  352  can also be used to distribute the unlabeled data  131  from the server  130  to the client device  310 . 
       FIG.  3 B  is a diagram of data flow for system  300 , according to an embodiment. Unlabeled data  131  from the server  130  can be transmitted to a client device  310  for processing by the machine learning module  361  on the client device. In one embodiment the unlabeled data  131  can be distributed to the various client devices via a deployment module  352 . The machine learning module  361  on the client device  310  can be trained via a training module  370  based on client data  332  within data storage  329  on the client device  310 . The client data  332  can include various types of client data, such as text message data, image data, application activity data, device activity data, and/or a combination of application activity and device activity data. 
     The machine learning module  361 , having been trained by the training module  370 , can generate at least one proposed label  333  for at least one unit of unlabeled data  131 . In one embodiment, a proposed label is generated for multiple units of unlabeled data  131 . Although multiple proposed labels can be generated, the number of privatized proposed labels transmitted to the server  130  may be limited based on a privacy budget configured for the client device  310 . In one embodiment, the mobile electronic device maintains a privacy budget that limits the amount of privatized data that can be transmitted to the server within a given timeframe. In such embodiment, once a certain amount of privatized data has been transmitted to the server, the mobile electronic device will refrain from sending any other privatized data for a period of time. 
     The privacy engine  353  can privatize the proposed label using one or more of a variety of privatization techniques, including but not limited to differential privacy techniques, homomorphic encryption, or secure multi-party computation. For each privatization technique, the server  130  will include corresponding logic to process the privatized data. Using a selected privatization technique, the privacy engine  353  generates at least one privatized proposed label  334 , which is encoded to mask the relationship between the proposed label  333  and the client device  310 . 
     The privatized proposed label  334  can be transmitted from the client device  310  to the server  130 . In one embodiment the privatized proposed label  334  is a tuple that contains a pairing of a privatized proposed label and an identifier of the unit of unlabeled data that corresponds with the proposed label. In one embodiment, instead of an identifier for the unit of unlabeled data, the tuple can directly include the unit of unlabeled data. Whether the unit of unlabeled data or an identifier for the unit is included can vary depending on the size of the individual units of unlabeled data to be labeled. For example, an identifier for image data can be transmitted with the proposed label, while proposed label for a character sequence can be directly included within the tuple. 
     In one embodiment the server  130  receives the privatized proposed label  334  via the receive module  351 . The receive module  351  can provide the various privatized labels from the various instances of the client device  310  to the frequency estimation module  341 . The frequency estimation module  341  can determine label frequency estimations  331  to estimate the most frequent proposed label for a given unit of unlabeled data  131 . The labeling and training module  330  can include a labeling model  330 A to label each unit of unlabeled data, for example, with the highest frequency proposed label for the unit, generating a set of labeled data  362 . A training module  330 B of the labeling and training module  330  can then add the labeled units of data to a training data set  363 . The training data set  363  can be used by the training module  330 B to train the machine learning module  135  to improve the accuracy of the machine learning model, resulting in an improved machine learning model  346 . 
       FIG.  4 A  is a flow diagram of a method  400  to improve the accuracy of a machine learning model via crowdsourced labeling of unlabeled data, according to an embodiment. The method  400  can be implemented in a server device, such as server device  130  as described herein. 
     In one embodiment, method  400  includes for the server to perform operation  401 , which includes to send a set of unlabeled data to a set of multiple mobile electronic devices. The set of multiple mobile electronic devices are each configured to generate proposed labels for elements in the set of unlabeled data. Each of the mobile electronic devices can include a machine learning model. The machine learning model can be one of a variety of machine learning models including, but not limited to a multiclass classification model or a regression model. The machine learning models can be implemented using a variety of techniques including convolutional or recurrent neural networks. 
     Method  400  additionally includes operation  402 , in which the server receives a set of proposed labels from the set of multiple mobile electronic devices. The set of proposed labels are encoded to mask individual contributors to the set of proposed labels. The client devices can encode the proposed labels using one or more of a variety of privacy preserving techniques, including differential privacy encoding, homomorphic encryption, secure multiparty compute, or other privatization techniques. In one embodiment, a client and server-side differential privacy algorithm is applied to the proposed labels, such as a count-mean-sketch algorithm or a multi-bit histogram algorithm. 
     Method  400  additionally includes operation  403 , in which the server processes the set of proposed labels to determine an estimate of a most frequent proposed label for each element in the unlabeled set of data. The processing can include applying a server-side count-mean-sketch or multi-bit histogram algorithm to generate a sketch or histogram from which frequencies of proposed labels can be estimated. From the frequency data, an estimate of a proposal frequency of each label can be determined for each element. The most frequently proposed label for each element in the set of unlabeled data can be used to generate labels for the elements in the set of unlabeled data. Method  400  can then perform operation  404  to add each element of unlabeled data and a corresponding most frequently proposed label for the element to a training data set. The method  400  additionally include operation  405 , in which the server trains the machine learning model using the training data set to generate an improved machine learning model. 
       FIG.  4 B  is a flow diagram of a method  410  to generate a privatized proposed label on a client device, according to an embodiment. The method  410  can be implemented in a client device, such as client device  310  as described herein. In one embodiment, and as described below, the client device is a mobile electronic device. However, other types of client devices can be used in some embodiments, such as desktop or laptop computing devices. 
     In one embodiment, method  410  includes operation  411  to select a set of client data on a mobile electronic device. A variety of different types of client data can be used to generate the training data set. For example, images on the device can be used to train an image classifier or text data can be used to train a word or character prediction model. In one embodiment, word sequences typed by a user can be used to train a predictive text model, which can be used to suggest words within a keyboard application. The specific type of data that is selected can be determined or limited based on privacy settings configured for the mobile electronic device. For example, a user of the mobile electronic device can opt-in or opt-out of the use of certain types of data for crowdsourced labeling. In one embodiment, various types of client data on the mobile electronic device are analyzed and the set of client data is selected from the types of client data that have elements sufficient in number to generate a viable training data set. A sufficient number of elements, in this case, is a number of elements over a mathematical threshold associated with the machine learning model that enables the model to be trained to a specified minimum level of accuracy. 
     Method  410  additionally includes operation  412  to generate a training set based on the selected set of client data. Generating the training set can include associating client data with labels associated with that client data. In one embodiment, the labels can be assigned to elements of client data by a user or can be labels that are auto-assigned using other classification logic available to the client device. In one embodiment the labels associated with images can be general labels with broad applicability, such as object labels (e.g., person, tree, house, apple, orange, cat, dog, etc.). In one embodiment, sequence data stored on the mobile electronic device can be partitioned for sequential learning. For example, the text sequence “Where are you” can be divided into a feature (“Where are”) and a label (“you”). Alternatively, a feature (“Where are you”) can have the label (“?”). Additionally, feature (“Where”) can be labeled (“are you?”) or (“is it?”) depending on the client data on the mobile electronic device. In one embodiment, application or device activity sequences stored on the mobile electronic device can be partitioned into features and labels. For example, a sequence of regular device activities including connecting to a power source, connecting to a Bluetooth device, leaving from or arriving to a specific location, or launching a specific application, can be analyzed to determine a set of regular activities performed on or with the mobile electronic device. Activities that are regularly performed in a specific sequence can be classified as sequence features having a label that enables prediction of the next activity in the sequence. For example, an activity feature including connecting to a certain Bluetooth audio device during a certain timeframe can be labeled as an application prediction (“Maps”) for the next application likely to be launched. If a map application is generally launched during this sequence, a navigation destination prediction (“Work”) can be applied as the label for the sequence feature. 
     Method  410  additionally includes operation  413  to train a machine learning model on the mobile electronic device using the training set. The training can be performed directly on the mobile electronic device, for example, when the device is idle and connected to a power source. The specific features and labels within the training set can vary between devices. Accordingly, the machine learning models on the mobile electronic devices will become individualized to each device. The training data set and the trained models cannot be transmitted from the mobile electronic devices to the server without leaking data that may be private to the user of the mobile electronic device. Instead, the server can send unlabeled data to the various mobile electronic devices. Thus, method  410  additionally includes operation  414  to receive a set of unlabeled data from a server. The set of unlabeled data can include one or more different types of data including but not limited to image data, text sequence data, and/or device activity data. The mobile electronic device can perform operation  415  to generate a proposed label for one or more elements in the set of unlabeled data. The number of proposed labels that are generated can vary, and in one embodiment is limited in part based on a privacy budget associated with the mobile electronic device. 
     In one embodiment, the unlabeled set of data is of the same type as the selected set of user data. In such embodiment, the mobile electronic device can communicate the type of data used to train the local machine learning model. In one embodiment, multiple types of unlabeled data are sent to the mobile electronic device. A data type can be associated with the various elements of unlabeled data and the mobile electronic device can generate labels for the type of data upon which the local model has been trained. In one embodiment, cryptographic or other algorithm can be used to enable the mobile electronic device and the server to agree on a type of data that will be used to train the machine learning model on the device. For example, the type of data to use can be determined based on function that uses a device identifier of the mobile electronic device as input. Alternatively, a hash of an identifier associated with a cloud services account of a user can be used, or any other combination of function and input that is known by the mobile electronic device and the server. 
     Method  410  additionally includes operation  416 , in which the mobile electronic device can transmit a privatized version of one or more proposed labels to the server. In one embodiment, operation  416  includes to transmit one or more tuples to the server, where each tuple includes a privatized proposed label and at least an identifier for an element in the set of unlabeled data. In one embodiment each element in the set of unlabeled data has an associated identifier that is known to the server. The server can use the identifier provided within the tuple to associate the privatized proposed label with the element for which the label is proposed. In one embodiment, where transmission and/or privacy budgets allow, the tuple includes the actual unit of unlabeled data along with the privatized proposed label for the unit of data. Whether the unit of unlabeled data or an identifier for the unit of unlabeled data can vary based on configuration, privacy/transmission budgets, or the type of data being labeled. 
     In some embodiments, the mobile electronic device can generate labels for multiple elements of unlabeled data. In such embodiments, operation  416  includes transmitting multipole tuples. The mobile electronic device can also be configured to send only one label or only a portion of a label to the server. Embodiments described herein are bandwidth efficient in that only a small amount of data is transmitted by the client device. For example, in one embodiment in which a count-mean-sketch algorithm is used, only 256 bits of data is transmitted to propose a label to the server. 
     The privatized version of the proposed label can be created using one or more of a variety of a privacy-preserving encoding described herein, such as but not limited to a differential privacy encoding or other privacy preserving techniques such as homomorphic encryption or secure multi-party compute. 
     The server can receive privatized proposed labels from multiple mobile electronic devices and aggregate the data and process the data using method  400  above. From the aggregated data, the server can estimate the most frequently applied label for elements of the unlabeled data sent to the mobile electronic devices and generate a training data set to enhance a server-based machine learning model. In some embodiments, method  400  and method  410  can proceed iteratively, in which a server-based machine learning model is enhanced using labels provided by multiple client devices and, after a period of time, the machine learning models on the mobile electronic devices can be updated. The updated machine learning models on the client devices can then be used to propose new labels to the server. 
     Proposed Label Privatization Via Differential Privacy. 
     In some embodiments, one or more differential privacy techniques are applied to the crowdsourced proposed labels to mask the identity of contributors of the proposed labels. As a general overview, local differential privacy introduces randomness to client user data prior to sharing the user data. Instead of having a centralized data source D={d 1 , . . . , d n }, each data entry d i  belongs to a separate client i. Given the transcript T i  of the interaction with client i, it is may not be possible for an adversary to distinguish T i  from the transcript that would have been generated if the data element were to be replaced by null. The degree of indistinguishability (e.g., degree of privacy) is parameterized by ε, which is a privacy parameter that represents a tradeoff between the strength of the privacy guarantee and the accuracy of the published results. Typically, ε is considered to be a small constant. In some embodiments, the ε value can vary based on the type of data to be privatized, with more sensitive data being privatized to a higher degree (smaller ε). The following is a formal definition of local differential privacy. 
     Let n be the number of clients in a client-server system, let Γ be the set of all possible transcripts generated from any single client-server interaction, and let T i  be the transcript generated by a differential privacy algorithm A while interacting with client i. Let d i ∈S be the data element for client i. Algorithm A is ε-locally differentially private if, for all subsets T⊆Γ, the following holds: 
     
       
         
           
             
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     Here, d i =null refers to the case where the data element for client i is removed. In other words, an adversary having n−1 data points of a data set cannot reliably test whether the nth data point was a particular value. Thus, a differentially privatized dataset cannot be queried in a manner that enables the determination of any particular user&#39;s data. 
     In one embodiment, a privatized multibit histogram model can be implemented on the client device and the server, with an optional transition to a count-mean-sketch privatization technique when the universe of labels exceeds a threshold. The multibit histogram model can send p bits to a server, where p corresponds to size of the universe of data values corresponding with potential proposed labels. The server can perform a summation operation to determine a frequency of user data values. The multibit histogram model can provide an estimated frequency variance of (c ε   2 −1)/4)n, where n is the number of users and 
               c   ɛ     =           e   ɛ     +   1         e   ɛ     -   1       .           
When the universe of data values corresponding with potential proposed labels exceeds a threshold, the server can use a count-mean-sketch differential privacy mechanism to estimate the frequency of proposed labels in a privatized manner.
 
       FIGS.  5 A- 5 C  illustrate exemplary privatized data encodings that can be used in embodiments described herein that implement privatization via differential privacy.  FIG.  5 A  illustrates proposed label encoding  500  on a client device.  FIG.  5 B  illustrates a proposed label histogram  510  on a server.  FIG.  5 C  illustrates proposed label frequency sketch  520  on a server. 
     As shown in  FIG.  5 A , in one embodiment a proposed label encoding  500  is created on a client device in which a proposed label value  502  is encoded into a proposed label vector  503 . The proposed label vector  503  is a one-hot encoding in which a bit is set that corresponds with a value associated with a proposed label generated by a client device. In the illustrated proposed label encoding  500 , the universe of labels  501  is the set of possible labels that can be proposed for an unlabeled unit of data provided to a client device by the server. The number of values in the universe of labels  501  is related to the machine-learning model that will be trained by the crowdsourced labeled data. For example, for a classifier that will be trained to infer a classification selected from a universe of p classifications, a universe size of p can be used for the universe of labels. However, such relationship is not required for all embodiments, and the size of the universe of labels is not fixed to any specific size. It should be noted that a vector is described herein for convenience and mathematical purposes, but any suitable data structure can be implemented, such as a string of bits, an object, etc. 
     As shown in  FIG.  5 B , in one embodiment the server can aggregate privatized proposed labels into a proposed label histogram  510 . For each unit of unlabeled data, the server can aggregate the proposed labels  512  and count the number of proposals  511  for each of the proposed labels  512 . The selected label  513  will be the proposed label with the greatest number of proposals  511 . 
     As shown in  FIG.  5 C , in one embodiment the server can generate a proposed label frequency sketch  520  for use with a count-mean-sketch differential privacy algorithm. The server can accumulate privatized proposed labels from multiple different client devices. Each client device can transmit a privatized encoding of a proposed label along with an index value (or a reference to the index value) of a random variant used when privatizing the proposed label. The random variant is a randomly selected variation on a proposed label to be privatized. Variants can correspond to a set of k values (or k index values) that are known to the server. The accumulated proposed labels can be processed by the server to generate the proposed label frequency sketch  520 . The frequency table can be indexed by the set of possible variant index values k. A row of the frequency table corresponding to the index value of the randomly selected variant is then updated with the privatized vector. More detailed operations of the multi-bit histogram and count-mean-sketch methods are further described below. 
       FIGS.  6 A- 6 B  are example processes  600 ,  610 ,  620  for encoding and differentially privatizing proposed labels to be transmitted to a server, according to embodiments described herein. In embodiments described herein, each client device that participates in crowdsourcing a label for a unit of server provided data can generate a proposed label for the unit of data and privatized the label before transmitting the label to the server. The proposed label can be a label within a universe of potential proposed labels, where a specific label value is associated with a proposed label selected by the client device. 
     In one embodiment, as shown in example process  600  of  FIG.  6 A , a specific value  601  is associated with a proposed label selected by the client device. The system can encode the label value  601  in the form of a vector  602 , where each position of the vector corresponds with a proposed label. The label value  601  can correspond to a vector or bit position  603 . For example, illustrated proposed label value Z corresponds to position  603  while potential proposed label values A and B correspond to different positions within the vector  602 . The vector  602  can be encoded by updating the value (e.g., setting the bit to 1) at position  603 . To account for any potential bias of a 0 or null value, the system may use an initialized vector  605 . In one embodiment, the initialized vector  605  can be a vector v←{−c ε } m . It should be noted that the values are used as mathematical terms, but can be encoded using bits (e.g., 0=+c ε , 1=−c ε ). Accordingly, vector  602  may use the initialized vector  605  to create an encoding  606  wherein the value (or bit) at position  603  is changed (or updated). For example, the sign of the value at position  603  can be flipped such that the value is c ε  (or +c ε ) and all other values remain −c ε  as shown (or vice versa). 
     The client device can then create a privatized encoding  608  by changing at least some of the values with a probability C p    609 , which may be a pre-determined probability. In one embodiment, the system can change the values via a flip the sign (e.g., (−) to (+), or vice versa) of the value. In one embodiment, probability C p    609  is equal to 
     
       
         
           
             
               
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     Accordingly, the label value  601  is now represented as a privatized encoding  608 , which individually maintains the privacy of the user that generated the proposed label. This privatized encoding  608  can be stored on the client device and subsequently transmitted to the server  130 . The server  130  can accumulate privatized encodings (e.g., vectors) from various client devices. The accumulated encodings may then be processed by the server for frequency estimation. In one embodiment, the server may perform a summation operation to determine a sum of the value of user data. In one embodiment, summation operation includes performing a summation operation on vectors received by the client devices. 
     In one embodiment, as shown in example process  610  of  FIG.  6 B , is an example process flow of differentially privatizing an encoding of user data to be transmitted to a server according to an embodiment of the disclosure. A client device can select a proposed label  611  to transmitted to the server. The proposed label  611  can be represented as a term  612  in any suitable format, where the term is a representation of the proposed label. In one embodiment, the term  612  can be converted to a numeric value using a hash function. As illustrated, a SHA256 hash function is used in one embodiment. However, any other hash function may also be used. For example, variants of SHA or other algorithms may be used such as SHA1, SHA2, SHA3, MD5, Blake2, etc. with various bit sizes. Accordingly, any hash function may be used in implementations given they are known to both the client and server. In one embodiment, a block cipher or another cryptographic function that is known to the client and server can also be used. 
     In one embodiment, computational logic on a client device can use a portion of a created hash value along with a variant  614  of the term  612  to address potential hash collisions when performing a frequency count by the server, which increases computational efficiency while maintaining a provable level of privacy. Variants  614  can correspond to a set of k values (or k index values) that are known to the server. In one embodiment, to create a variant  614 , the system can append a representation of an index value  616  to the term  612 . As shown in this example, an integer corresponding to the index value (e.g., “1,”) may be appended to the term  612  to create a variant (e.g., “1,Apple”, or “Apple1”, etc.). The system can then randomly select a variant  619  (e.g., variant at random index value r). Thus, the system can generate a random hash function  617  by using a variant  614  (e.g., random variant  619 ) of the term  612 . The use of variants enables the creation of a family of k hash functions. This family of hash functions is known to the server and the system can use the randomly selected hash function  617  to create a hash value  613 . In one embodiment, in order to reduce computations, the system may only create the hash value  613  of the randomly selected variant  619 . Alternatively, the system may create a complete set of hash values (e.g., k hash values), or hash values up to the randomly selected variant r. It should be noted that a sequence of integers is shown as an example of index values, but other forms of representations (e.g., various number of character values) or functions (e.g., another hash function) may also be used as index values given that they are known to both the client and server. 
     Once a hash value  613  is generated, the system may select a portion  618  of the hash value  613 . In this example, a 16-bit portion may be selected, although other sizes are also contemplated based on a desired level of accuracy or computational cost of the differential privacy algorithm (e.g., 8, 16, 32, 64, etc. number of bits). For example, increasing the number of bits (or m) increases the computational (and transmission) costs, but an improvement in accuracy may be gained. For instance, using 16 bits provides 2 16 −1 (e.g., approximately 65 k) potential unique values (or m range of values). Similarly, increasing the value of the variants k, increases the computational costs (e.g., cost to compute a sketch), but in turn increases the accuracy of estimations. In one embodiment, the system can encode the value into a vector, as in  FIG.  6 A , where each position of the vector can correspond to a potential numerical value of the created hash value  613 . 
     For example, process flow  620  of  FIG.  6 B  illustrates that the created hash value  613 , as a decimal number, can be correspond to a vector/bit position  625 . Accordingly, a vector  626  may be encoded by updating the value (e.g., setting the bit to 1) at position  625 . To account for any potential bias of a 0 or null value, the system may use an initialized vector  627 . In one embodiment, the initialized vector  627  may be a vector v←{−c ε } m . It should be noted that the values are used as mathematical terms, but may be encoded using bits (e.g., 0=+c ε , 1=−c ε ). Accordingly, vector  626  may use the initialized vector  627  to create an encoding  628  wherein the value (or bit) at position  625  is changed (or updated). For example, the sign of the value at position  625  may be flipped such that the value is c ε  (or +c ε ) and all other values remain −c ε  as shown (or vice versa). 
     The system can then create a privatized encoding  632  by changing at least some of the values with a probability C p    633 , where 
               C   p     =         e   ɛ         e   ɛ     +   1       .           
In one embodiment, the system can change a value by flipping the sign (e.g., (−) to (+), or vice versa) of the value. Accordingly, the proposed label  611  is now represented as a privatized encoding  632 , which individually maintains the privacy of the user when the privatized encoding  632  of the proposed label  611  is aggregated by the server.
 
       FIGS.  7 A- 7 D  are block diagrams of multibit histogram and count-mean-sketch models of client and server algorithms according to an embodiment.  FIG.  7 A  shows an algorithmic representation of the client-side process  700  of the multibit histogram model as described herein.  FIG.  7 B  shows an algorithmic representation of the server-side process  710  of the multibit histogram model as described herein.  FIG.  7 C  shows an algorithmic representation of a client-side process  720  of a count-mean-sketch model as described herein.  FIG.  7 D  shows an algorithmic representation of a server-side process  730  of a count-mean-sketch model as described herein. The client-side process  700  and server-side process  710  can use the multibit histogram model to enable privacy of crowdsourced data while maintaining the utility of the data. Client-side process  700  can initialize vector ν←{−c ε } m . Where the user is to transmit d∈[p], client-side process  700  can be applied to flip the sign of ν[h(d)], where h is a random hash function. To ensure differential privacy, client-side process  700  can flip the sign of each entry ν with a probability of 
                 e   ɛ         e   ɛ     +   1       .         
The client-side process  720  can also use hash functions to compress frequency data for when the universe of proposed labels exceeds a threshold.
 
     As shown  FIG.  7 A , client-side process  700  can receive input including a privacy parameter ε, a universe size p, and data element d∈S, as shown at block  701 . At block  702 , client-side process  700  can set a constant 
               c   ɛ     ←         e   ɛ     +   1         e   ɛ     -   1             
and initialize vector v←{−c ε } p , as shown in block  702 . Constant c ε  allows noise added to maintain privacy and remain unbiased. Added noise should be large enough to mask individual items of user data, but small enough to allow any patterns in the dataset to appear. As shown at block  703  client-side process  700  can then set ν[d]←c ε  and, at block  704 , sample vector b∈{−1, +1} p , with each b j  being independent and identically distributed and outputs +1 with probability
 
                 e   ɛ         e   ɛ     +   1       .         
As shown at block  705 , client-side process  700  can then generate a privatized vector
 
               v   priv     =       {       (           v   ⁡     [   j   ]       *     b   j       +   1     2     )     ,     ∀     j   ∈     [   p   ]           }     .           
At block  706 , client-side algorithm  700  can return vector ν priv , which is a privatized version of vector ν.
 
     As shown in  FIG.  7 B , server-side process  710  aggregates the client-side vectors and, given input including privacy parameter c, universe size p, and data element s∈S, whose frequency is to be estimated, can return an estimated frequency based on aggregated data received from crowdsourcing client devices. As shown at block  711 , server-side process  710  (e.g., A server ), given privacy parameters and a universe size p, can obtain n vectors ν 1 , . . . , ν n  corresponding to the data set D={d 1 , . . . , d n }, such that ν i ←A client  (ε, p, d i ). At block  712 , server-side process  710  can initialize a counter f s  (e.g., f s ←0). Server-side process  710 , for each tuple ν i , i∈[n], can set f s =f s +ν i [s], as shown at block  713 . At block  714 , server-side process  710  can return f s , which is a frequency of the value of user data amongst the aggregate data set. 
     Client-side process  700  and server-side process  710  provide privacy and utility. Client-side process  700  and server-side process  710  are jointly locally differentially private. Client-side process  700  is ε-locally differentially private and server-side process  710  only accesses the privatized data. For arbitrary output v∈{−c ε , c ε } p , the probability of observing the output is similar whether the user is present or not. For example, in the case of an absent user, the output of A client  (ε, p, h, φ) can be considered, where φ is the null element. By the independence of each bit flip, 
                 Pr   ⁡     [         A   client     ⁡     (     ɛ   ,   p   ,   h   ,   d     )       =   v     ]         Pr   ⁡     [         A   client     ⁡     (     ɛ   ,   p   ,   h   ,   φ     )       =   v     ]         ≤     e     -   ɛ             
Similarly,
 
     
       
         
           
             
               
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     Server-side process  710  also has a utility guarantee for frequency estimation. Privacy and utility are generally tradeoffs for differential privacy algorithms. For a differential privacy algorithm to achieve maximal privacy, the output of the algorithm may not be a useful approximation of the actual data. For the algorithm to achieve maximal utility, the output may not be sufficiently private. The multibit histogram model described herein achieves c-local differential privacy while achieving optimal utility asymptotically. 
     The overall concepts for the count-mean-sketch algorithm are similar to those of multi-bit histogram, excepting that data to be transferred is compressed when the universe size p becomes very large. The server can use a sketch matrix M of dimension k×m to aggregate the privatized data. 
     As shown  FIG.  7 C , a client-side process  720  can receive input including a data element d∈S, a privacy parameter ε, a universe size p, and a set of k hash functions H={h 1 , h 2 , . . . h k } that each map [p] to [m], can select random index j from [k] to determine hash function h j , as shown at block  721 . Client-side process  720  can then set a constant 
               c   ɛ     ←         e   ɛ     +   1         e   ɛ     -   1             
and initialize vector v←{−c ε } m , as shown in block  722 . Constant c ε  allows noise added to maintain privacy and remain unbiased. Added noise should be large enough to mask individual items of user data, but small enough to allow any patterns in the dataset to appear.
 
     As shown at block  723  client-side process  720  can use randomly selected hash function h j  to set ν[h j (d)]←c ε . At block  724 , client-side process  720  can sample vector b∈{−1, +1} m , with each b j  being independent and identically distributed and outputs +1 with probability 
                 e   ɛ         e   ɛ     +   1       .         
As shown at block  725 , client-side process  720  can then generate a privatized vector
 
               v   priv     =       {       (           v   ⁡     [   j   ]       *     b   j       +   1     2     )     ,     ∀     j   ∈     [   m   ]           }     .           
At block  726 , client-side process  720  can return vector ν priv , which is a privatized version of vector ν, and randomly selected index j.
 
     As shown in  FIG.  7 D , a server-side process  730  can aggregate client-side vectors and from client-side process  720 . Server-side process  730  can receive input including a set of n vectors and indices {(ν 1 , j 1 ), . . . , (ν n , j n )}, a privacy parameter ε, and a set of k hash functions H={h 1 , h 2 , . . . h k } that each map [p] to [m], as shown at block  731 . Server-side process  730  can then initialize matrix M←0, where M has k rows and m columns, such that M∈{0} k×m , as shown at block  732 . As shown at block  733 , for each tuple (ν i , j i ), i∈[n], server-side process  730  can add ν i  to the j i  row of M, such that M[j i ][:]←M[j i ][:]+ν i . At block  734 , the server-side process  730  can return sketch matrix M. Given the sketch matrix M, it is possible to estimate the count for entry d∈S by de-biasing the counts and averaging over the corresponding hash entries in M. 
     While specific examples of proposed label privatization via multibit histogram and/or count-mean-sketch differential privacy techniques are described above, embodiments are not limited to any specific differential privacy algorithm for implementing privacy of the crowdsourced labels. Embodiments can be configured to use any local differential privacy algorithm that enables the privatized estimation of aggregate frequency data from multiple sources, while masking the contributor of each individual element of data to the data set. Additionally, the privacy techniques are not explicitly limited to the user of differential privacy algorithms. As described herein, homomorphic encryption techniques can be applied, such that encrypted values received from client devices can be summed on the server without revealing the privatized data to the server. For example, the client devices can employ a homomorphic encryption algorithm to encrypt proposed labels and send the proposed labels to the server. The server can then perform a homomorphic addition operation to sum the encrypted proposed labels without requiring the knowledge of the unencrypted proposed labels. In one embodiment, secure multi-party computation techniques can also be applied, such that the client device and the server can jointly compute aggregated values for the proposed labels without exposing the user data directly to the sever. 
       FIG.  8    illustrates data that can be labeled in a privatized manner, according to embodiments. The machine learning module  361  of  FIG.  3 A  includes a machine learning model that can be trained using a variety of different types of data on the client device  310 . 
     In one embodiment, text data can be used to generate training data to train a machine learning model on the client device to perform text sequence labeling  802 . Training to generate labels for sequential text data can be performed by dividing text sequences on the client device into features and labels and training a local machine learning model on the features and labels. For example, “Where are you” can be divided into a feature (“Where are”) and a label (“you”). “I am good” can be divided into the feature (“I am”) and the label (“good”). This data can be used to train machine learning models within machine learning modules on each client device, resulting in machine learning models that are individualized for each device based on the local data on the device. Proposed labels can then be generated for unlabeled data sent to the client devices from the server. 
     In one embodiment, activity sequences  804  can be labeled, such as but not limited to application launch sequences, application activity sequences, device activity sequences, or combinations of application and device activities. Application or device activity sequences recognized by the device can be used to train a machine learning model in the client device in a similar manner as text sequence labeling. For example, a sequence of regular device activities including connecting to a power source, connecting to a Bluetooth device, leaving from or arriving to a specific location, or launching a specific application, can be analyzed to determine a set of regular activities performed on or with the mobile electronic device. Activities that are regularly performed in a specific sequence can be classified as sequence features having a label that enables prediction of the next activity in the sequence. 
     In one embodiment, labeled images  806  on a client device can also be used to train a machine learning model on a client device. For example, images or photographs stored or associated with a client device that have been labeled in some manner can be used to train a machine learning model on the client device. In one embodiment, relevant labels are general descriptions associated with images (e.g., flowers, sunset, etc.). In one embodiment the labels can be applied to the images by a user or automatically via automated image labeling or captioning logic provided by an image or photo management program. 
       FIG.  9 A  illustrates device activity sequences that can be learned in a privatized manner, according to an embodiment. A user device  904  is illustrated, where the user device  904  can be a variant of any form of user device described herein, including, for example, client device  310  as described herein. User device  904  can include a machine learning module such as the machine learning module  361  of client device  310 . The user device  904  can execute a variety of functions on behalf of a user, including functions performed by one or more applications executing on the user device  904 . In one embodiment, application activity data  906  can be stored by the user device  904  that records at least a subset of application launches or in-app activities. Applications and activities within the application activity data  906  can be assigned numerical values. The numerical values associated with the applications and activities performed by the user device  904  can be encoded as data sequences that can be used to train a machine learning model of a machine learning module on the user device  904 . The trained machine learning model can be used to train unlabeled sequences provided by a server. 
     In one embodiment the launching of the services review application  901  can be detected and stored in the application activity data  906 . In one embodiment the user device  904  can store at least a subset of in-app activity within the application activity data  906 . For example, the services review application  901  can optionally donate in-application activity to the application activity data  906  and/or searches performed within the services review application  901 . In one embodiment, the user device  904  can detect a subset of in-app activity performed on the user device  904 . As an example of the activities described above, a user can launch a services review application  901  that provides user reviews of service or goods providers (e.g., restaurants, retail stores, repair shops, etc.). The user device  904  can launch the services review application  901  in response to receipt of a graphical interface request or a virtual assistant request. The user can perform a search for a service or goods provider, for example via the graphical interface or voice interface of the user device  904 . The user device  904  can then display reviews of one or more providers. The user can then launch a map application  903  on the user device, for example, to enable the user to determine a location of one or more service or goods providers. The user can then launch a rideshare application  905  on the user device  904 . The rideshare application  905 , can be separate from or associated with the map application  903 . Alternatively, the user can direct the user device  904  to enable turn-by-navigation  913 , which can be a feature of the map application  903  or a feature provided by a separate map or navigation application. 
     In one embodiment, each of the application launches and/or application activities can be encoded as a numerical sequence within the application activity data  906 . The numerical sequence can be divided into feature and label portions. The feature and label portions can be used to train the machine learning model. The trained machine learning model can then propose labels for unlabeled sequences provided by the server. A proposed label can be selected and privatized by a privacy engine on a client device using a privacy preserving encoding technique described herein. The privatized label can then be transmitted to the server. Embodiments are not limited to the specific examples shown. Additional application activity sequences that can be learned include purchase sequences within an online application or media store (e.g., app store) or in-app purchase sequences within an application. 
       FIG.  9 B  illustrates device activity  920  that can be used to train a predictor model on a client device. As shown in  FIG.  9 B , a variety of device activity  920  can be sampled and used to train predictive models for application and/or device activities. Event data  928  can be gathered from multiple devices for a user and combined into aggregated user data. Event data  928  can be gathered from a variety of user devices, including wearable electronic devices, mobile devices such as smartphones and tablet computing devices, laptop computing devices, and desktop computing devices. Event data  928  includes but is not limited to user action data  922 , context data  924 , and device status data  926 . 
     User action data  922  includes, for example, device motion data, in app actions, and app in focus data. Motion data can include raw accelerometer data for the device as well as processed accelerometer data that indicates information such as a number of steps taken by a user, distance travelled, exercise data, flights of stairs taken, standing versus sitting metrics, and the like. In app actions include activity performed within an application, such as purchases made in an online app store or media store, in-app purchases made within an application, websites visited by a web browser, photographs taken by a camera application, and other user actions within a given application. App in focus data includes information about which applications are active and the duration which the user makes use of those applications. 
     Context data  924  includes context information associated with other event data  928 , such as user actions  922  or device status  926 . For example, for each user action  922 , context data  924  can be gathered to provide additional information about those actions. For example, if a user regularly runs for exercise, the time and location of those runs can be recorded as context data  924  by the active device of the user during the run. During the run, proximity information can also be recorded, such as proximity of the active device to devices of other users or to geographic points of interest for the user. 
     Event data  928  can also include device status  926 , such as Wi-Fi device status, including signal strength analysis and available access points to the device. Device status  926  can also include battery information including current and historical battery energy level, charge status, and the percentage of battery usage that is devoted to particular activities or applications. 
     The various elements of event data  928  and other types of device activity  920  can be converted into event sequences  930  and partitioned into feature and element data. In one embodiment, feature data can include a user action, a context, and an associated device status. The label can be a prediction that would be made based on the combination of action, context and status. The feature and element data can be added training data that is used to train machine learning models on the client device. The machine learning models can then generate proposed labels for unlabeled server data, which can then be used to train a predictive model on the server device. 
       FIG.  10    illustrates compute architecture  1000  on a client device that can be used to enable on-device supervised training and inferencing using machine learning algorithms, according to embodiments described herein. In one embodiment, compute architecture  1000  includes a client labeling framework  1002  that can be configured to leverage a processing system  1020  on a client device. The client labeling framework  1002  includes a vision/image framework  1004 , a language processing framework  1006 , and one or more other frameworks  1008 , which each can reference primitives provided by a core machine learning framework  1010 . The core machine learning framework  1010  can access resources provided via a CPU acceleration layer  1012 , neural network processor acceleration layer  1013  and a GPU acceleration layer  1014 . The CPU acceleration layer  1012 , neural network processor acceleration layer  1013 , and the GPU acceleration layer  1014  each facilitate access to a processing system  1020  on the various client devices described herein. The processing system includes an application processor  1022 , a neural network processor  1023 , and a graphics processor  1024 , each of which can be used to accelerate operations of the core machine learning framework  1010  and the various higher-level frameworks that operate via primitives provided via the core machine learning framework. The application processor  1022  and graphics processor  1024  include hardware that can be used to perform general-purpose processing and graphics specific processing for the core machine learning framework  1010 . The neural network processor  1023  includes hardware that is tuned specifically to accelerate processing operations for artificial neural networks. The neural network processor  1023  can increase speed at which neural network operations are performed, but is not required to enable the operation of the client labeling framework  1002 . Labeling operations can be performed using the application processor  1022  and/or the graphics processor  1024 . 
     In one embodiment, the various frameworks and hardware resources of the compute architecture  1000  can be used for inferencing operations via a machine learning model, as well as training operations for a machine learning model. For example, a client device can use the compute architecture  1000  to perform supervised learning via a machine learning model as described herein, such as but not limited to a CNN, RNN, or LSTM model. The client device can then use the trained machine learning model to infer proposed labels for a unit of unlabeled data provided by a server. 
     Additional Exemplary Computing Devices 
       FIG.  11    is a block diagram of a device architecture  1100  for a mobile or embedded device, according to an embodiment. The device architecture  1100  includes a memory interface  1102 , a processing system  1104  including one or more data processors, image processors and/or graphics processing units, and a peripherals interface  1106 . The various components can be coupled by one or more communication buses or signal lines. The various components can be separate logical components or devices or can be integrated in one or more integrated circuits, such as in a system on a chip integrated circuit. 
     The memory interface  1102  can be coupled to memory  1150 , which can include high-speed random-access memory such as static random-access memory (SRAM) or dynamic random-access memory (DRAM) and/or non-volatile memory, such as but not limited to flash memory (e.g., NAND flash, NOR flash, etc.). 
     Sensors, devices, and subsystems can be coupled to the peripherals interface  1106  to facilitate multiple functionalities. For example, a motion sensor  1110 , a light sensor  1112 , and a proximity sensor  1114  can be coupled to the peripherals interface  1106  to facilitate the mobile device functionality. One or more biometric sensor(s)  1115  may also be present, such as a fingerprint scanner for fingerprint recognition or an image sensor for facial recognition. Other sensors  1116  can also be connected to the peripherals interface  1106 , such as a positioning system (e.g., GPS receiver), a temperature sensor, or other sensing device, to facilitate related functionalities. A camera subsystem  1120  and an optical sensor  1122 , e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, can be utilized to facilitate camera functions, such as recording photographs and video clips. 
     Communication functions can be facilitated through one or more wireless communication subsystems  1124 , which can include radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of the wireless communication subsystems  1124  can depend on the communication network(s) over which a mobile device is intended to operate. For example, a mobile device including the illustrated device architecture  1100  can include wireless communication subsystems  1124  designed to operate over a GSM network, a CDMA network, an LTE network, a Wi-Fi network, a Bluetooth network, or any other wireless network. In particular, the wireless communication subsystems  1124  can provide a communications mechanism over which a media playback application can retrieve resources from a remote media server or scheduled events from a remote calendar or event server. 
     An audio subsystem  1126  can be coupled to a speaker  1128  and a microphone  1130  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions. In smart media devices described herein, the audio subsystem  1126  can be a high-quality audio system including support for virtual surround sound. 
     The I/O subsystem  1140  can include a touch screen controller  1142  and/or other input controller(s)  1145 . For computing devices including a display device, the touch screen controller  1142  can be coupled to a touch sensitive display system  1146  (e.g., touch-screen). The touch sensitive display system  1146  and touch screen controller  1142  can, for example, detect contact and movement and/or pressure using any of a plurality of touch and pressure sensing technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with a touch sensitive display system  1146 . Display output for the touch sensitive display system  1146  can be generated by a display controller  1143 . In one embodiment, the display controller  1143  can provide frame data to the touch sensitive display system  1146  at a variable frame rate. 
     In one embodiment, a sensor controller  1144  is included to monitor, control, and/or processes data received from one or more of the motion sensor  1110 , light sensor  1112 , proximity sensor  1114 , or other sensors  1116 . The sensor controller  1144  can include logic to interpret sensor data to determine the occurrence of one of more motion events or activities by analysis of the sensor data from the sensors. 
     In one embodiment, the I/O subsystem  1140  includes other input controller(s)  1145  that can be coupled to other input/control devices  1148 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, and/or a pointer device such as a stylus, or control devices such as an up/down button for volume control of the speaker  1128  and/or the microphone  1130 . 
     In one embodiment, the memory  1150  coupled to the memory interface  1102  can store instructions for an operating system  1152 , including portable operating system interface (POSIX) compliant and non-compliant operating system or an embedded operating system. The operating system  1152  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, the operating system  1152  can be a kernel. 
     The memory  1150  can also store communication instructions  1154  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers, for example, to retrieve web resources from remote web servers. The memory  1150  can also include user interface instructions  1156 , including graphical user interface instructions to facilitate graphic user interface processing. 
     Additionally, the memory  1150  can store sensor processing instructions  1158  to facilitate sensor-related processing and functions; telephony instructions  1160  to facilitate telephone-related processes and functions; messaging instructions  1162  to facilitate electronic-messaging related processes and functions; web browser instructions  1164  to facilitate web browsing-related processes and functions; media processing instructions  1166  to facilitate media processing-related processes and functions; location services instructions including GPS and/or navigation instructions  1168  and Wi-Fi based location instructions to facilitate location based functionality; camera instructions  1170  to facilitate camera-related processes and functions; and/or other software instructions  1172  to facilitate other processes and functions, e.g., security processes and functions, and processes and functions related to the systems. The memory  1150  may also store other software instructions such as web video instructions to facilitate web video-related processes and functions; and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, the media processing instructions  1166  are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. A mobile equipment identifier, such as an International Mobile Equipment Identity (IMEI)  1174  or a similar hardware identifier can also be stored in memory  1150 . 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. The memory  1150  can include additional instructions or fewer instructions. Furthermore, various functions may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
       FIG.  12    is a block diagram of a computing system  1200 , according to an embodiment. The illustrated computing system  1200  is intended to represent a range of computing systems (either wired or wireless) including, for example, desktop computer systems, laptop computer systems, tablet computer systems, cellular telephones, personal digital assistants (PDAs) including cellular-enabled PDAs, set top boxes, entertainment systems or other consumer electronic devices, smart appliance devices, or one or more implementations of a smart media playback device. Alternative computing systems may include more, fewer and/or different components. The computing system  1200  can be used to provide the computing device and/or a server device to which the computing device may connect. 
     The computing system  1200  includes bus  1235  or other communication device to communicate information, and processor(s)  1210  coupled to bus  1235  that may process information. While the computing system  1200  is illustrated with a single processor, the computing system  1200  may include multiple processors and/or co-processors. The computing system  1200  further may include memory  1220 , such as random access memory (RAM) or other dynamic storage device coupled to the bus  1235 . The memory  1220  may store information and instructions that may be executed by processor(s)  1210 . The memory  1220  may also be used to store temporary variables or other intermediate information during execution of instructions by the processor(s)  1210 . 
     The computing system  1200  may also include read only memory (ROM)  1230  and/or another data storage device  1240  coupled to the bus  1235  that may store information and instructions for the processor(s)  1210 . The data storage device  1240  can be or include a variety of storage devices, such as a flash memory device, a magnetic disk, or an optical disc and may be coupled to computing system  1200  via the bus  1235  or via a remote peripheral interface. 
     The computing system  1200  may also be coupled, via the bus  1235 , to a display device  1250  to display information to a user. The computing system  1200  can also include an alphanumeric input device  1260 , including alphanumeric and other keys, which may be coupled to bus  1235  to communicate information and command selections to processor(s)  1210 . Another type of user input device includes a cursor control  1270  device, such as a touchpad, a mouse, a trackball, or cursor direction keys to communicate direction information and command selections to processor(s)  1210  and to control cursor movement on the display device  1250 . The computing system  1200  may also receive user input from a remote device that is communicatively coupled via one or more network interface(s)  1280 . 
     The computing system  1200  further may include one or more network interface(s)  1280  to provide access to a network, such as a local area network. The network interface(s)  1280  may include, for example, a wireless network interface having antenna  1285 , which may represent one or more antenna(e). The computing system  1200  can include multiple wireless network interfaces such as a combination of Wi-Fi, Bluetooth®, near field communication (NFC), and/or cellular telephony interfaces. The network interface(s)  1280  may also include, for example, a wired network interface to communicate with remote devices via network cable  1287 , which may be, for example, an Ethernet cable, a coaxial cable, a fiber optic cable, a serial cable, or a parallel cable. 
     In one embodiment, the network interface(s)  1280  may provide access to a local area network, for example, by conforming to IEEE 802.11 standards, and/or the wireless network interface may provide access to a personal area network, for example, by conforming to Bluetooth standards. Other wireless network interfaces and/or protocols can also be supported. In addition to, or instead of, communication via wireless LAN standards, network interface(s)  1280  may provide wireless communications using, for example, Time Division, Multiple Access (TDMA) protocols, Global System for Mobile Communications (GSM) protocols, Code Division, Multiple Access (CDMA) protocols, Long Term Evolution (LTE) protocols, and/or any other type of wireless communications protocol. 
     The computing system  1200  can further include one or more energy sources  1205  and one or more energy measurement systems  1245 . Energy sources  1205  can include an AC/DC adapter coupled to an external power source, one or more batteries, one or more charge storage devices, a USB charger, or other energy source. Energy measurement systems include at least one voltage or amperage measuring device that can measure energy consumed by the computing system  1200  during a predetermined period of time. Additionally, one or more energy measurement systems can be included that measure, e.g., energy consumed by a display device, cooling subsystem, Wi-Fi subsystem, or other frequently used or high-energy consumption subsystem. 
     In some embodiments, the hash functions described herein can utilize specialized hardware circuitry (or firmware) of the system (client device or server). For example, the function can be a hardware-accelerated function. In addition, in some embodiments, the system can use a function that is part of a specialized instruction set. For example, the hardware can use an instruction set which may be an extension to an instruction set architecture for a particular type of microprocessors. Accordingly, in an embodiment, the system can provide a hardware-accelerated mechanism for performing cryptographic operations to improve the speed of performing the functions described herein using these instruction sets. 
     In addition, the hardware-accelerated engines/functions are contemplated to include any implementations in hardware, firmware, or combination thereof, including various configurations which can include hardware/firmware integrated into the SoC as a separate processor, or included as special purpose CPU (or core), or integrated in a coprocessor on the circuit board, or contained on a chip of an extension circuit board, etc. 
     It should be noted that the term “approximately” or “substantially” may be used herein and may be interpreted as “as nearly as practicable,” “within technical limitations,” and the like. In addition, the use of the term “or” indicates an inclusive or (e.g. and/or) unless otherwise specified. 
     As described above, one aspect of the present technology is the gathering and use of data available from various specific and legitimate sources to enable crowdsource learning of sequential data. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to identify a specific person. Such personal information data can include demographic data, location-based data, online identifiers, telephone numbers, email addresses, social media IDs, home addresses, data or records relating to a user&#39;s health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to learn new words, improve keyboard layouts, improve auto-correct engines for keyboards, and to enable an electronic device to better anticipate the needs of a user. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used, in accordance with the user&#39;s preferences, to provide insights into their general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals. 
     The present disclosure contemplates that those entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities would be expected to implement and consistently apply privacy practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. Such information regarding the use of personal data should be prominently and easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate uses only. Further, such collection/sharing should occur only after receiving the consent of the users or other legitimate basis specified in applicable law. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations which may serve to impose a higher standard. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app. 
     Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user&#39;s privacy. De-identification may be facilitated, when appropriate, by removing identifiers, controlling the amount or specificity of data stored (e.g., collecting location data at city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods such as differential privacy. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, crowdsourcing of sequences can be performed over a large number of users and is based on aggregated, non-personal information data. A large number of individual users can opt out of sending data to the sequence learning server and overall trends can still be detected. 
     In the foregoing description, example embodiments of the disclosure have been described. It will be evident that various modifications can be made thereto without departing from the broader spirit and scope of the disclosure. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense. The specifics in the descriptions and examples provided may be used anywhere in one or more embodiments. The various features of the different embodiments or examples may be variously combined with some features included and others excluded to suit a variety of different applications. Examples may include subject matter such as a method, means for performing acts of the method, at least one machine-readable medium including instructions that, when performed by a machine cause the machine to perform acts of the method, or of an apparatus or system according to embodiments and examples described herein. Additionally, various components described herein can be a means for performing the operations or functions described herein. 
     Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. 
     One embodiment provides for a data processing system comprising a memory device to store instructions and one or more processors to execute the instructions stored on the memory device. The instructions cause the data processing system to perform operations comprising sending an unlabeled set of data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the unlabeled set of data, wherein each of the mobile electronic devices include a variant of a first machine learning model; receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors of each proposed label in the set of proposed labels; processing the set of proposed labels to determine a most frequent proposed label for the unlabeled set of data; adding the unlabeled set of data and the most frequent proposed label to a first training set; and training a second machine learning model using the first training set, the second machine learning model on a server device. 
     One embodiment provides for a non-transitory machine readable medium storing instructions to cause one or more processors to perform operations comprising sending an unlabeled set of data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the unlabeled set of data, wherein each of the mobile electronic devices include a first machine learning model; receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors to the set of proposed labels; processing the set of proposed labels to determine an estimate of a most frequent proposed label for the unlabeled set of data; adding the unlabeled set of data and corresponding most frequent proposed labels to a first training set; and training a second machine learning model using the first training set, the second machine learning model on a server device. 
     One embodiment provides for a data processing system on a mobile electronic device, the data processing system comprising a memory device to store instructions and one or more processors to execute the instructions stored on the memory device. The instructions cause the one or more processors to select a set of data on the mobile electronic device; generate a training set based on selected data; train a first machine learning model using the training set; receive an unlabeled set of data from a server; generate proposed labels for elements of the unlabeled set of data; and transmit a privatized version of one or more proposed labels to the server. 
     One embodiment provides for a non-transitory machine readable medium storing instructions to cause one or more processors to perform operations comprising selecting a set of data on a mobile electronic device; generating a training set based on selected data; training a first machine learning model using the training set, the first machine learning model trained on the mobile electronic device; receiving an unlabeled set of data from a server; generating proposed labels for elements of the unlabeled set of data; and transmitting a privatized version of a proposed label to the server. 
     One embodiment provides for a data processing system comprising a memory device to store instructions and one or more processors to execute the instructions stored on the memory device. The instructions cause the data processing system to perform operations comprising sending an unlabeled set of data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the unlabeled set of data, wherein each of the mobile electronic devices include a variant of a first machine learning model; receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors of each proposed label in the set of proposed labels; processing the set of proposed labels to determine a most frequent proposed label for the unlabeled set of data; adding the unlabeled set of data and the most frequent proposed label to a first training set; and training a second machine learning model using the first training set, the second machine learning model on a server device. 
     One embodiment provides for a non-transitory machine readable medium storing instructions to cause one or more processors to perform operations comprising sending an unlabeled set of data to a set of multiple mobile electronic devices, the set of multiple mobile electronic devices to generate a set of proposed labels for the unlabeled set of data, wherein each of the mobile electronic devices include a first machine learning model; receiving a set of proposed labels for the unlabeled set of data from the set of multiple mobile electronic devices, the set of proposed labels encoded to mask individual contributors to the set of proposed labels; processing the set of proposed labels to determine an estimate of a most frequent proposed label for the unlabeled set of data; adding the unlabeled set of data and corresponding most frequent proposed labels to a first training set; and training a second machine learning model using the first training set, the second machine learning model on a server device. 
     One embodiment provides for a data processing system on a mobile electronic device, the data processing system comprising a memory device to store instructions and one or more processors to execute the instructions stored on the memory device. The instructions cause the one or more processors to select a set of data on the mobile electronic device; generate a training set based on selected data; train a first machine learning model using the training set; receive an unlabeled set of data from a server; generate proposed labels for elements of the unlabeled set of data; and transmit a privatized version of one or more proposed labels to the server. 
     One embodiment provides for a non-transitory machine readable medium storing instructions to cause one or more processors to perform operations comprising selecting a set of data on a mobile electronic device; generating a training set based on selected data; training a first machine learning model using the training set, the first machine learning model trained on the mobile electronic device; receiving an unlabeled set of data from a server; generating proposed labels for elements of the unlabeled set of data; and transmitting a privatized version of a proposed label to the server. 
     Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description above. Accordingly, the true scope of the embodiments will become apparent to the skilled practitioner upon a study of the drawings, specification, and following claims.

Metadata:
Filing Date: 20190829
Publication Date: 20230725
Grant Date: 20230725
Priority Date: 20180928
Inventors: BHOWMICK, ABHISHEK
ROGERS, RYAN M.
VAISHAMPAYAN, UMESH S.
VYRROS, ANDREW H.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N3/08", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/044", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/045", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/10", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/044", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/045", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/10", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/044", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/045", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 69945962