Patent application EP19153349A (EP), filed 2019-01-23
Publication number: EP3525388A2
Title: PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS
Abstract: [0001]    One embodiment provides for a mobile electronic device comprising a non-transitory machine-readable medium to store instructions, the instructions to cause the mobile electronic device to receive a set of labeled data from a server; receive a unit of data from the server, the unit of data of a same type of data as the set of labeled data; determine a proposed label for the unit of data via a machine learning model on the mobile electronic device, the machine learning model to determine the proposed label for the unit of data based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.
IPC codes: G06N  20/    00            A I
H04L   9/    00            A I
Claims: number: 1
text: A mobile electronic device comprising:
number: 2
text: The mobile electronic device as in claim 1, wherein the machine learning model is included in a system image installed to the mobile electronic device and or wherein the machine learning model is part of a generative adversarial network.
number: 3
text: The mobile electronic device as in claim 1 or 2, the mobile electronic device to receive the machine learning model from the server.
number: 4
text: The mobile electronic device as in claim 1, 2 or 3, wherein the set of unlabeled data associated with the mobile electronic device is at least temporarily stored on the mobile electronic device and preferably wherein, the mobile electronic device is associated with an account of a remote storage service, and a subset of the set of unlabeled data is retrieved from the remote storage service.
number: 5
text: The mobile electronic device of any preceding claim, wherein to determine the proposed label for the unit of data from the server, the machine learning model is to cause the one or more processors to:
number: 6
text: The mobile electronic device as in claim 5, the machine learning model to determine the proposed label for the unit of data from the server based additionally on a set of labeled data associated with the mobile electronic device that is distinct from the set of labeled data from the server.
number: 7
text: The mobile electronic device as in any preceding claim, wherein the machine learning model is part of a generative adversarial network.
number: 8
text: The mobile electronic device as in any preceding claim wherein the privacy algorithm is to cause the mobile electronic device to transform the proposed label to mask an individual contributor of the proposed label; and or
wherein the privacy algorithm includes a secure multi-party compute operation or a homomorphic encryption operation; and or
wherein the privacy algorithm is a differential privacy algorithm; and or
wherein the privacy algorithm is a histogram-based differential privacy algorithm.
number: 9
text: The mobile electronic device as in any preceding claim, wherein the unit of data from the server, the set of labeled data from the server, and the set of unlabeled data associated with the mobile electronic device each have an image data type or a text data type and the machine learning model on the mobile electronic device is an image classifier or a text data classifier.
number: 10
text: A data processing system comprising:
number: 11
text: The data processing system as in claim 10, the operations additionally including training the second machine learning model on the data processing system using the training set, wherein the second machine learning model is an update of the first machine learning model, wherein preferably the set of labeled data and the unit of data sent to the set of multiple mobile electronic devices each include image data or text data, and wherein the first and second machine learning models include image classifiers or text data classifiers.
number: 12
text: The data processing system as in claim 10 or 11, wherein the set of proposed labels are at least partially encoded using a homomorphic encryption algorithm and processing the set of proposed labels includes performing a homomorphic addition operation.
number: 13
text: The data processing system as in claim 10, 11 or 12 wherein the set of proposed labels are at least partially encoded using a differential privacy algorithm and processing the set of proposed labels includes generating a proposed label histogram.
number: 14
text: The data processing system as in any of claims 10 to 13, wherein the set of proposed labels are at least partially encoded using a differential privacy algorithm and processing the set of proposed labels includes applying a count-mean-sketch algorithm to the set of proposed labels.
Office action: search_report, dated 2019-08-14
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: US9275347
ref_type: patent
severity: obviousness
ref_id: US9594741
ref_type: patent
severity: obviousness
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ref_type: npl
severity: obviousness
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ref_type: npl
severity: background
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ref_type: npl
severity: background
ref_id: - BIN LIU ET AL, "Cloud-enabled privacy-preserving collaborative learning for mobile sensing", EMBEDDED NETWORK SENSOR SYSTEMS, ACM, 2 PENN PLAZA, SUITE 701 NEW YORK NY 10121-0701 USA, (20121106), doi:10.1145/2426656.2426663, ISBN 978-1-4503-1169-4, pages 57 - 70, XP058029982 [Y] 10-14 * page  60  - page  63 *
ref_type: npl
severity: obviousness
ref_id: - BRILAND HITAJ ET AL, "Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20170224), XP080748410 [A] 10-14 * page  1  - page  6 *
ref_type: npl
severity: background
Final disposition: pending (2019-08-14)