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公开(公告)号：US10552631B2
公开(公告)日：2020-02-04
申请号：US16297464
申请日：2019-03-08
Applicant: Apple Inc.
Inventor： Yannick L. Sierra , Abhradeep Guha Thakurta , Umesh S. Vaishampayan , John C. Hurley , Keaton F. Mowery , Michael Brouwer
Abstract: One embodiment provides a system that implements a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES to provide an efficient mechanism for implementing differential privacy.
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公开(公告)号：US20190097978A1
公开(公告)日：2019-03-28
申请号：US16159473
申请日：2018-10-12
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.
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公开(公告)号：US10154054B2
公开(公告)日：2018-12-11
申请号：US15640266
申请日：2017-06-30
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudinger , Vipul Ved Prakash , Arnaud Legendre , Steven Duplinsky
Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
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公开(公告)号：US20170359364A1
公开(公告)日：2017-12-14
申请号：US15640266
申请日：2017-06-30
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudinger , Vipul Ved Prakash , Arnaud Legendre , Steven Duplinsky
CPC classification number: H04L63/1425 , G06F17/2235 , G06F17/2735 , G06F17/276 , G06F21/6254 , G06N99/005 , H04L63/0421
Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
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公开(公告)号：US10701042B2
公开(公告)日：2020-06-30
申请号：US16159473
申请日：2018-10-12
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
IPC: H04L29/06 , G06N20/00 , G06F16/36 , G06N3/12 , G06F40/205 , G06F40/242 , G06F40/279 , G06F40/284 , G06F17/16 , G06F16/35 , G06F7/58 , G06F9/30
Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.
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Patent Agency Ranking
公开(公告)号：US10454962B2
公开(公告)日：2019-10-22
申请号：US16159481
申请日：2018-10-12
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vipul Ved Prakash , Arnaud Legendre , Steven Duplinsky
Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
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公开(公告)号：US10229282B2
公开(公告)日：2019-03-12
申请号：US15275284
申请日：2016-09-23
Applicant: Apple Inc.
Inventor： Yannick L. Sierra , Abhradeep Guha Thakurta , Umesh S. Vaishampayan , John C. Hurley , Keaton F. Mowery , Michael Brouwer
Abstract: The system described may implement a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations of the system may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES. Accordingly, the system provides an efficient mechanism for implementing differential privacy.
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公开(公告)号：US10133725B2
公开(公告)日：2018-11-20
申请号：US15477921
申请日：2017-04-03
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.
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公开(公告)号：US20180039619A1
公开(公告)日：2018-02-08
申请号：US15477921
申请日：2017-04-03
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
CPC classification number: G06F17/2765 , G06F17/16 , G06F17/2705 , G06F17/2735 , G06F17/277 , G06F17/30737 , G06N99/005
Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.
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公开(公告)号：US20170357820A1
公开(公告)日：2017-12-14
申请号：US15275284
申请日：2016-09-23
Applicant: Apple Inc.
Inventor： Yannick L. Sierra , Abhradeep Guha Thakurta , Umesh S. Vaishampayan , John C. Hurley , Keaton F. Mowery , Michael Brower
CPC classification number: G06F21/6218 , G06F21/6245 , H04L9/0631 , H04L9/0861 , H04L63/0421 , H04L63/0435
Abstract: The system described may implement a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations of the system may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES. Accordingly, the system provides an efficient mechanism for implementing differential privacy.
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