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    Maintaining differential privacy for database query results
    1.
    发明授权
    Maintaining differential privacy for database query results 有权

    公开(公告)号：US11086915B2

    公开(公告)日：2021-08-10

    申请号：US16708307

    申请日：2019-12-09

    Applicant: Apple Inc.

    Inventor： Mona Chitnis ,  Abhishek Bhowmick ,  Lucas O. Winstrom ,  Koray Mancuhan ,  Stephen D. Fleischer

    IPC: G06F16/00 ,  G06F16/335 ,  G06F16/33 ,  G06F21/62 ,  G06F16/16 ,  G06F16/338

    Abstract: The subject technology for maintaining differential privacy for database query results receives a query for a database that contains user data. The subject technology determines that the query is permitted for the database based at least in part on a privacy policy associated with the database. The subject technology determines that performing the query will not exceed a query budget for the database. The subject technology, when the query is permitted and performing the query will not exceed the query budget, performs the query on the database and receiving results from the query. The subject technology selects a differential privacy algorithm for the results based at least in part on a query type of the query. The subject technology applies the selected differential privacy algorithm to the results to generate differentially private results. The subject technology provides the differentially private results.

    Distributed labeling for supervised learning
    3.
    发明授权
    Distributed labeling for supervised learning 有权

    公开(公告)号：US12260331B2

    公开(公告)日：2025-03-25

    申请号：US18225656

    申请日：2023-07-24

    Applicant: Apple Inc.

    Inventor： Abhishek Bhowmick ,  Ryan M. Rogers ,  Umesh S. Vaishampayan ,  Andrew H. Vyrros

    IPC: G06N3/08 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/10

    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.

    Privatized apriori algorithm for sequential data discovery
    6.
    发明授权
    Privatized apriori algorithm for sequential data discovery 有权

    公开(公告)号：US11055492B2

    公开(公告)日：2021-07-06

    申请号：US16271702

    申请日：2019-02-08

    Applicant: Apple Inc.

    Inventor： Abhishek Bhowmick ,  Ryan M. Rogers ,  Umesh S. Vaishampayan ,  Kartik R. Venkatraman

    IPC: G06F40/232 ,  G06F40/30 ,  G06N20/00 ,  G06F40/242

    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

    PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS
    8.
    发明申请
    PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS 审中-公开

    公开(公告)号：US20190244138A1

    公开(公告)日：2019-08-08

    申请号：US15892246

    申请日：2018-02-08

    Applicant: Apple Inc.

    Inventor： Abhishek Bhowmick ,  Andrew H. Vyrros ,  Ryan M. Rogers

    IPC: G06N99/00 ,  H04L29/08

    CPC classification number: G06N20/00 ,  H04L9/008 ,  H04L67/10

    Abstract: 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.

    PRIVATE FEDERATED LEARNING WITH PROTECTION AGAINST RECONSTRUCTION
    10.
    发明申请
    PRIVATE FEDERATED LEARNING WITH PROTECTION AGAINST RECONSTRUCTION 有权

    公开(公告)号：US20210166157A1

    公开(公告)日：2021-06-03

    申请号：US16501132

    申请日：2020-01-17

    Applicant: Apple Inc.

    Inventor： Abhishek Bhowmick ,  John Duchi ,  Julien Freudiger ,  Gaurav Kapoor ,  Ryan M. Rogers

    IPC: G06N20/20 ,  G06N5/04

    Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to perform operations comprising receiving a machine learning model from a server at a client device, training the machine learning model using local data at the client device, generating an update for the machine learning model, the update including a weight vector that represents a difference between the received machine learning model and the trained machine learning model, privatizing the update for the machine learning model, and transmitting the privatized update for the machine learning model to the server.

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