FEDERATED DATA STANDARDIZATION USING DATA PRIVACY TECHNIQUES

Methods, systems, and computer program products for federated data standardization using data privacy techniques are provided herein. A computer-implemented method includes obtaining multiple datasets from multiple clients in accordance with one or more data privacy techniques; determining one or more similar data columns across at least a portion of the multiple datasets; generating one or more column labels for the one or more similar data columns; standardizing at least a portion of data within the one or more similar data columns by processing the one or more generated column labels using at least one federated learning technique; and performing one or more automated actions based at least in part on results of the standardizing of the at least a portion of data within the one or more similar data columns.

BACKGROUND

The present application generally relates to information technology and, more particularly, to data management techniques. More specifically, consider the setting of federated learning (FL), wherein different users possess private data which cannot be shared with other users due to privacy constraints. In such a setting, assume the implementation of a machine learning task such as training a machine learning algorithm across various decentralized devices, each holding sensitive or otherwise private data. Further, such a task may include rendering data consistent across the different devices, thereby enabling accurate execution of the machine learning algorithm. However, challenges exist with respect to maintaining data privacy across the different devices.

SUMMARY

In one embodiment of the present invention, techniques for federated data standardization using data privacy techniques are provided. An exemplary computer-implemented method can include obtaining multiple datasets from multiple clients in accordance with one or more data privacy techniques, determining one or more similar data columns across at least a portion of the multiple datasets, and generating one or more column labels for the one or more similar data columns. The method can also include standardizing at least a portion of data within the one or more similar data columns by processing the one or more generated column labels using at least one federated learning technique, and performing one or more automated actions based at least in part on results of the standardizing of the at least a portion of data within the one or more similar data columns.

Another embodiment of the invention or elements thereof can be implemented in the form of a computer program product tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another embodiment of the invention or elements thereof can be implemented in the form of a system including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another embodiment of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media).

DETAILED DESCRIPTION

As described herein, an embodiment of the present invention includes federated data standardization using data privacy techniques. One or more embodiments include, in a federated learning setting, standardizing column values and column labels and/or names of participating clients' private data (for example, for supervised machine learning settings) by learning a mapping of similar columns across different clients' data. As further detailed herein, such an embodiment can include standardizing column labels and/or names of the clients' data by computing one or more feature labels and embedding the computed feature label(s) to learn feature name standardization across different clients' data. As used herein, “embedding” refers to a method of deriving a numerical vector representation of one or more labels such that the numerical vector can further be used in building a machine learning model for feature name standardization. Additionally, as used above and further herein, features refer to columns of datasets owned by different clients. A purpose of feature name standardization can include deriving the same feature name across two or more clients (in a privacy-preserving manner) such that the clients would have and/or use the same label. Similarly, another purpose of column value standardization can include deriving the same value standardization across the two or more clients.

Accordingly, one or more embodiments include using federated learning techniques to standardize clients' private data without violating one or more client and/or data privacy constraints. Such an embodiment includes determining similar data columns across various clients' data, computing column values and column labels and/or names for at least portions of the similar data columns. Additionally, such an embodiment includes utilizing at least a portion of the column values and column labels and/or names for standardizing data across the various clients using one or more federated learning settings without violating one or more client privacy constraints. By way of illustration an example privacy constraint might include a principle that clients do not want to share column data or labels directly with any other client or a central server of a given federated learning system. Rather, each client can share, for example, some higher-order statistics of their data (such as derived embeddings of each column data, for instance) with the server.

At least one embodiment can include implementation in contexts of data standardization before any machine learning models are built in federated settings. In such settings, clients typically cannot share data across nodes, and as such, there is no global visibility of data. In the absence of such visibility, it is important to ensure, for example, that column X from a dataset in client1 corresponds to column Y from a dataset in client2; otherwise the model learning becomes increasingly challenging.

In other example contexts, many organizations adopt hybrid cloud infrastructure, and the orchestration by organizations associated with such infrastructure facilitates access hybrid data as well as analysis of such data. However, compliance, security, data privacy, and network performance often make hybrid data difficult for the organizations to move together (or pool) across different cloud environments. To address such technical challenges associated with hybrid data environments, a federated learning paradigm (which, for example, does not require data pooling) can be introduced in accordance with one or more embodiments.

As detailed herein, such an embodiment includes feature label standardization, which can include learning and/or mapping similar columns across different clients' data and embedding one or more feature labels into such client data to facilitate learning of feature name standardization(s) across the different clients.

Additionally, at least one embodiment includes learning and/or identifying similar columns across different sets of client data using graph node anchoring techniques. Such an embodiment can include, at each individual client, constructing a graph (G_i) using at least a portion of the client's available private data (i). In constructing such a graph, the nodes of the graph represent the columns in the client's dataset, and edge weights represent correlations among pairs of the columns.

Also, one or more embodiments include determining edge weights between a pair of columns (C1, C2) in a graph (G_i). Such a determination can include repeating the following steps for different values of k and using the best k value to derive the edge weight of (C1, C2). The first such step includes computing k clusters and/or groups using the data in column C1 and referring to that set of k clusters/groups as D1. Similarly, a related step includes computing k clusters and/or groups using the data in column C2 and referring to that set of k clusters/groups as D2. In at least one embodiment, such clusters and/or groups within each column are computed using one or more clustering algorithms (e.g., a K-means clustering algorithm) based on the similarity of the values in the column.

The second such step includes constructing a graph (S), wherein the set of nodes in graph (S) include the set of clusters/groups in D1 and D2. Accordingly, there would be a total of 2 k nodes in graph (S), and the edges and corresponding weights of these edges in graph (S) can be designed as follows. For each group in D1 and D2, the word embedding of each data value/point is computed. By way of illustration, because each client cannot share its own column's data with the central server, each client partitions each of its columns into k groups of data points. For each group (g1) from column 1, one or more embodiments include computing a word embedding vector, which can be computed as follows. Consider, for example, that the group g1 contains m data points. Using an algorithm such as, e.g., the word2vec algorithm, such an embodiment includes computing an embedding vector for each data point in the group g1. Then, to compute an embedding vector for the group g1 itself, such an embodiment includes taking the average of the word embedding vectors of data points in g1. Note that these word embeddings possess similar information as in the original data points but in a different feature space, such that the privacy of the clients is not breached by sharing these embedding vectors.

Also, from each group (g1) in D1 to each group (g2) in D2, the average distance between word embeddings (of g1 and g2) is computed. In at least one embodiment, the distance between two groups (e.g., g1 and g2) can be computed using one or more distance metrics such as Euclidean distance (L2 norm). The smaller the computed distance, the more similarity exists between these two groups of data. Accordingly, the weight of the edge between these two groups (g1, g2) is inversely proportional to the computed average distance therebetween. Subsequently, the maximum weight matching of graph (S) can be determined to identify the most similar pairs of groups between D1 and D2, wherein the weight of this matching represents the similarity between the two columns C1 and C2. The intuition behind “maximum weight matching” includes selecting a set of edges with maximum weight from the given graph such that each node is covered or incident on at most one edge in the matching. In at least one embodiment, one or more polynomial time algorithms can be used to determine maximum weight matching of a given graph. Further, in one or more embodiments, graph (G_i) can be shared and/or output to a server by the client in question.

Additionally, in at least one embodiment, at the server, graph node anchoring (implemented to learn and/or identify similar columns across different sets of client data) can include the following. Upon receiving k graphs (G_1, G_2, . . . , G_K) from the given set of clients, the server solves a graph node anchoring problem wherein a function is learned to map nodes from one graph to nodes of at least one other graph by leveraging the graph connectivity structure. As used herein, graph connectivity structure refers to the structure of a graph (e.g., G_i) shared by a client (e.g., client i) with the server. Typically, the structure of the graph refers to the set of edges among the nodes along with how the nodes and/or edges are connected with each other. Additionally, as detailed herein, graph connectivity structure can be leveraged to learn a mapping of the nodes in the graph (e.g., G_i) to the nodes in a different graph (e.g., graph G_j). To accomplish this, potential properties of the connectivity structure that can be leveraged can include, for example, the degree of each node, the average distance of each node to other nodes, egonet (i.e., the 1-hop neighborhood of each node), etc. Note that in one or more example embodiments, the clients/nodes would contain similar data regarding a particular task (e.g., employee details of different office locations, etc.).

As also detailed herein, one or more embodiments include learning feature name standardization across different clients by utilizing feature label embedding. In such an embodiment, the following protocol can be executed for each column. At each individual client, by making use of the values in each column, a machine learning model is trained to predict the name of the column and its corresponding embedding vector. By way of example, one or more embodiments include attempting to determine a possible descriptive name of a column by making use of the values in that column itself. At least one machine learning model can be learned to determine such a name for each column, and the corresponding embedding vector can be determined using an algorithm such as, e.g., the word2vec algorithm. Such column values can often include “short text,” and short text clustering techniques can be used to generate and/or determine one or more clusters. As used herein, short text refers to one word or a small collection of words (as opposed to one or more sentences, for example). Because column names are often formed with one word or a small number of words, such names often qualify to be “short texts.” Such clusters can then be used to derive one or more meaningful cluster labels.

At least one embodiment can then include deriving one or more word embeddings for each of the cluster labels and aggregating the word embeddings to derive and/or generate a single unified label embedding vector. Note that each cluster can include a collection of similar-looking column labels. For each column label (L) in a given cluster (C), such an embodiment includes using an algorithm such as, for example, the word2vec embedding algorithm, to derive an embedding vector for the label (L). Then, by using these embedding vectors for the labels in cluster (C), such an embodiment can include computing an average of all of these embedding vectors to determine an embedding vector for the cluster (C) itself. Further, in such an embodiment, each client can then share and/or output the derived label embedding vector (e_i) with a server.

At the server, in one or more embodiments, upon receiving the K embedding vectors (e_1, e_2, . . . , e_K) from at least a portion of the clients, a computation is performed to derive a single label embedding vector. By way merely of example, such a computation can include aggregating all of the embedding vectors.

As also detailed herein, feature values standardization can include agreement on column mapping across clients, agreement on column output, and standardization using at least one program by example. In one or more embodiments, such a program by example can include a representative table to illustrate how the embedding vector that represents the column label of each client should be given a label in plain English, and that this English label is thereby consistent across all clients having the same column.

FIG.1is a diagram illustrating system architecture, according to an embodiment of the invention. By way of illustration,FIG.1depicts data standardization operation input102, which can include, for example, identification of a particular data standardization operation selected by at least one data scientist and/or other user. Such input102is provided to server106. Also depicted inFIG.1are client devices104-1,104-2,104-3, . . .104-N, collectively referred to herein as client devices104. Metadata are shared (without violating one or more privacy constraints) by client devices104to server106, and server106, using federated learning infrastructure, executes the data standardization operation from input102using at least a portion of the metadata provided by client devices104. Subsequently, based at least in part on the execution of the data standardization operation, server106outputs standardized versions of data, residing in each client device104, to the given client devices104, while also preserving data privacy in accordance with one or more privacy constraints. Regarding the arrow from the server106that indicates “new global model,” this refers to the model learned at the server by making use of the inputs and/or information shared by the client devices104. Because clients cannot share the data directly, the clients can only share some higher-order statistics (e.g., such as embedding vectors). The server106makes use of these informative embedding vectors to learn a model at the server-side to derive and/or generate a global model that has collective information from the client devices104. Additionally, the different shapes illustrated inFIG.1in connection with the lines from the client devices104to the server106merely indicate that the information shared by each client device104to the sever106(for the data standardization problem) is different.

The client devices104illustrated inFIG.1and otherwise detailed herein may comprise, for example, mobile telephones, laptop computers, tablet computers, desktop computers or other types of computing devices. Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.”

The client devices104in some embodiments may also comprise respective computers associated with a particular company, organization or other enterprise. Numerous other operating scenarios involving a wide variety of different types and arrangements of processing devices and networks are possible, as will be appreciated by those skilled in the art.

Further, it is to be appreciated that the term “client” in this context and elsewhere herein is intended to be broadly construed so as to encompass, for example, human, hardware, software or firmware entities, as well as various combinations of such entities.

FIG.2is a flow diagram illustrating techniques according to an embodiment of the present invention. Step202includes obtaining multiple datasets from multiple clients in accordance with one or more data privacy techniques. Step204includes determining one or more similar data columns across at least a portion of the multiple datasets. In at least one embodiment, determining similar data columns includes identifying the one or more similar data columns across at least a portion of the multiple datasets using one or more graph node anchoring techniques. In such an embodiment, using one or more graph node anchoring techniques includes, for each of the multiple datasets, constructing a graph using at least a portion of available sensitive data contained therein, and wherein in constructing the graph, each node of the graph represents a column in the given dataset, and each edge weight represents a correlation among pairs of columns in the given dataset. Additionally, using one or more graph node anchoring techniques can include mapping one or more nodes from one of the constructed graphs to one or more nodes of at least one other of the constructed graphs by leveraging graph connectivity structure information.

Step206includes generating one or more column labels for the one or more similar data columns. Step208includes standardizing at least a portion of data within the one or more similar data columns by processing the one or more generated column labels using at least one federated learning technique. In at least one embodiment, processing the one or more generated column labels using at least one federated learning technique includes learning feature name standardization across at least a portion of the multiple datasets by utilizing one or more feature label embedding techniques and based at least in part on the one or more generated column labels. In such an embodiment, learning feature name standardization across at least a portion of the multiple datasets includes training at least one machine learning model, using values in at least a portion of the one or more similar data columns, to predict a name of the given data column and at least one corresponding embedding vector of the given data column. By way of example, the values can include text, and training the at least one machine learning model can include using one or more text clustering techniques to generate one or more clusters among the at least a portion of the one or more similar data columns.

Further, at least one embodiment can also include determining one or more cluster labels based at least in part on the one or more generated clusters, deriving one or more word embeddings for each of the one or more cluster labels, and generating a single unified label embedding vector based at least in part on aggregating the derived word embeddings.

Step210includes performing one or more automated actions based at least in part on results of the standardizing of the at least a portion of data within the one or more similar data columns. In at least one embodiment, performing one or more automated actions includes outputting corresponding portions of the standardized data to the respective ones of the multiple clients. Additionally or alternatively, one or more embodiments can include generating and/or providing software implementing the techniques depicted inFIG.2as a service in a cloud environment.

Input/output or I/O devices (including, but not limited to, keyboards308, displays306, pointing devices, and the like) can be coupled to the system either directly (such as via bus310) or through intervening I/O controllers (omitted for clarity).

Additionally, it is understood in advance that implementation of the teachings recited herein are not limited to a particular computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any type of computing environment now known or later developed.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Virtualization layer70provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers71; virtual storage72; virtual networks73, including virtual private networks; virtual applications and operating systems74; and virtual clients75. In one example, management layer80may provide the functions described below. Resource provisioning81provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing82provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.

In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal83provides access to the cloud computing environment for consumers and system administrators. Service level management84provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment85provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

At least one embodiment of the present invention may provide a beneficial effect such as, for example, automating federated data standardization using data privacy techniques.