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公开(公告)号：US12079260B2
公开(公告)日：2024-09-03
申请号：US17399030
申请日：2021-08-10
申请人： Apple Inc.
IPC分类号： G06F16/00 , G06F16/16 , G06F16/33 , G06F16/335 , G06F16/338 , G06F21/62
CPC分类号： G06F16/335 , G06F16/168 , G06F16/3331 , G06F16/338 , G06F21/6218
摘要： 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.
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公开(公告)号：US12052315B2
公开(公告)日：2024-07-30
申请号：US17129579
申请日：2020-12-21
申请人： Apple Inc.
发明人： Stephen Cosman , Kalu Onuka Kalu , Marcelo Lotif Araujo , Michael Chatzidakis , Thi Hai Van Do , Alexis Hugo Louis Durocher , Guillaume Tartavel , Sowmya Gopalan , Vignesh Jagadeesh , Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , Ryan M. Rogers
IPC分类号： H04L67/1097 , G06F16/2457 , G06F16/438 , G06F16/44 , G06F18/214 , G06F21/62 , G06N3/063 , G06N20/00 , G06V10/774 , G06V10/82 , H04L67/00
CPC分类号： H04L67/1097 , G06F16/24578 , G06F16/438 , G06F16/447 , G06F18/2148 , G06F21/6254 , G06N3/063 , G06N20/00 , G06V10/7747 , G06V10/82 , H04L67/34
摘要： Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.
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公开(公告)号：US11086915B2
公开(公告)日：2021-08-10
申请号：US16708307
申请日：2019-12-09
申请人： Apple Inc.
IPC分类号： G06F16/00 , G06F16/335 , G06F16/33 , G06F21/62 , G06F16/16 , G06F16/338
摘要： 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.
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公开(公告)号：US20180349636A1
公开(公告)日：2018-12-06
申请号：US15805591
申请日：2017-11-07
申请人： Apple Inc.
CPC分类号： G06F21/6245 , G06F1/28 , G06F3/0482 , G06F3/0483 , H04L9/3239 , H04L63/0428 , H04L67/02 , H04L67/22 , H04L2209/34 , H04L2209/42 , H04W12/02
摘要： Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. In one embodiment, a differential privacy mechanism is implemented using a count-mean-sketch technique that can reduce resource requirements required to enable privacy while providing provable guarantees regarding privacy and utility. For instance, the mechanism can provide the ability to tailor utility (e.g. accuracy of estimations) against the resource requirements (e.g. transmission bandwidth and computation complexity).
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公开(公告)号：US11055492B2
公开(公告)日：2021-07-06
申请号：US16271702
申请日：2019-02-08
申请人： Apple Inc.
IPC分类号： G06F40/232 , G06F40/30 , G06N20/00 , G06F40/242
摘要： 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.
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公开(公告)号：US20210192078A1
公开(公告)日：2021-06-24
申请号：US17129579
申请日：2020-12-21
申请人： Apple Inc.
发明人： Stephen Cosman , Kalu Onuka Kalu , Marcelo Lotif Araujo , Michael Chatzidakis , Thi Hai Van Do , Alexis Hugo Louis Durocher , Guillaume Tartavel , Sowmya Gopalan , Vignesh Jagadeesh , Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , Ryan M. Rogers
摘要： Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.
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公开(公告)号：US20190244138A1
公开(公告)日：2019-08-08
申请号：US15892246
申请日：2018-02-08
申请人： Apple Inc.
摘要： 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.
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公开(公告)号：US11710035B2
公开(公告)日：2023-07-25
申请号：US16556066
申请日：2019-08-29
申请人： Apple Inc.
摘要： 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.
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公开(公告)号：US20210166157A1
公开(公告)日：2021-06-03
申请号：US16501132
申请日：2020-01-17
申请人： Apple Inc.
摘要： 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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公开(公告)号：US10776511B2
公开(公告)日：2020-09-15
申请号：US15805611
申请日：2017-11-07
申请人： Apple Inc.
发明人： Kevin W. Decker , Conrad Shultz , Steve Falkenburg , Mateusz Rajca , Abhishek Bhowmick , Andrew H. Vyrros , Umesh S. Vaishampayan
IPC分类号： H04L29/06 , G06F21/62 , H04L9/32 , H04L29/08 , H04W12/02 , G06F3/0483 , G06F1/28 , G06F3/0482
摘要： Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. One embodiment uses a differential privacy mechanism to enhance a user experience by inferring potential user preferences from analyzing crowdsourced user interaction data. Based on a statistical analysis of user interactions in relation to various features or events, development efforts with respect to application behavior may be refined or enhanced. For example, user interactions in relation to the presentation of content such as content from online sources may be analyzed. Accordingly, presentation settings or preferences may be defined based on the crowdsourced user interaction data.
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