Patents by Inventor Andrew H. Vyrros
Andrew H. Vyrros has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11227063Abstract: 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).Type: GrantFiled: September 14, 2020Date of Patent: January 18, 2022Assignee: Apple Inc.Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
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Publication number: 20200410134Abstract: 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).Type: ApplicationFiled: September 14, 2020Publication date: December 31, 2020Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
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Publication number: 20200356685Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.Type: ApplicationFiled: July 24, 2020Publication date: November 12, 2020Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
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Patent number: 10776511Abstract: 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.Type: GrantFiled: November 7, 2017Date of Patent: September 15, 2020Assignee: Apple Inc.Inventors: Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
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Patent number: 10726139Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.Type: GrantFiled: September 30, 2017Date of Patent: July 28, 2020Assignee: Apple Inc.Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
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Patent number: 10701042Abstract: 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.Type: GrantFiled: October 12, 2018Date of Patent: June 30, 2020Assignee: Apple Inc.Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
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Publication number: 20200104705Abstract: 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.Type: ApplicationFiled: August 29, 2019Publication date: April 2, 2020Inventors: Abhishek Bhowmick, Ryan M. Rogers, Umesh S. Vaishampayan, Andrew H. Vyrros
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Patent number: 10599867Abstract: 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).Type: GrantFiled: November 7, 2017Date of Patent: March 24, 2020Assignee: Apple Inc.Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
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Patent number: 10599868Abstract: 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 identifying particular websites that exhibit particular characteristics. In one embodiment, websites that are associated with a high resource consumption are identified. High resource consumption can be identified based on threshold of particular resources such as processor, memory, network bandwidth, and power usage.Type: GrantFiled: November 7, 2017Date of Patent: March 24, 2020Assignee: Apple Inc.Inventors: Gavin Barraclough, Christophe Dumez, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
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Patent number: 10574770Abstract: Modifying a notification on one client device can trigger the generation and transmission of a silent notification to another client device that is associated with the same user account. The silent notification can include instructions to query for and modify a similar notification, if present, on the other client device. Silent notifications that are undeliverable can be stored in offline storage and delivery can be reattempted at a later point in time.Type: GrantFiled: May 14, 2018Date of Patent: February 25, 2020Assignee: Apple Inc.Inventors: Andrew H. Vyrros, Matthew Elliott Shepherd, Dylan Ross Edwards, Justin Wood, Daniel Ben Pollack, Pierre de Filippis, Jonathan Drummond, Justin Santamaria, Greg Novick
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Patent number: 10454962Abstract: 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.Type: GrantFiled: October 12, 2018Date of Patent: October 22, 2019Assignee: Apple Inc.Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
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Publication number: 20190244138Abstract: 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.Type: ApplicationFiled: February 8, 2018Publication date: August 8, 2019Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Ryan M. Rogers
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Publication number: 20190097978Abstract: 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.Type: ApplicationFiled: October 12, 2018Publication date: March 28, 2019Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
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Publication number: 20190089797Abstract: Modifying a notification on one client device can trigger the generation and transmission of a silent notification to another client device that is associated with the same user account. The silent notification can include instructions to query for and modify a similar notification, if present, on the other client device. Silent notifications that are undeliverable can be stored in offline storage and delivery can be reattempted at a later point in time.Type: ApplicationFiled: May 14, 2018Publication date: March 21, 2019Applicant: Apple Inc.Inventors: Andrew H. Vyrros, Matthew Elliott Shepherd, Dylan Ross Edwards, Justin Wood, Daniel Ben Pollack, Pierre de Filippis, Jonathan Drummond, Justin Santamaria, Greg Novick
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Publication number: 20190068628Abstract: 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.Type: ApplicationFiled: October 12, 2018Publication date: February 28, 2019Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
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Patent number: 10154054Abstract: 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.Type: GrantFiled: June 30, 2017Date of Patent: December 11, 2018Assignee: Apple Inc.Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
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Publication number: 20180349620Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.Type: ApplicationFiled: September 30, 2017Publication date: December 6, 2018Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
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Publication number: 20180349636Abstract: 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).Type: ApplicationFiled: November 7, 2017Publication date: December 6, 2018Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
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Publication number: 20180349637Abstract: 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.Type: ApplicationFiled: November 7, 2017Publication date: December 6, 2018Inventors: Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
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Publication number: 20180349638Abstract: 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 identifying particular websites that exhibit particular characteristics. In one embodiment, websites that are associated with a high resource consumption are identified. High resource consumption can be identified based on threshold of particular resources such as processor, memory, network bandwidth, and power usage.Type: ApplicationFiled: November 7, 2017Publication date: December 6, 2018Inventors: Gavin Barraclough, Christophe Dumez, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan