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).

  • Patent number: 11227063
    Abstract: 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: Grant
    Filed: September 14, 2020
    Date of Patent: January 18, 2022
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
  • Publication number: 20200410134
    Abstract: 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: Application
    Filed: September 14, 2020
    Publication date: December 31, 2020
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
  • Publication number: 20200356685
    Abstract: 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: Application
    Filed: July 24, 2020
    Publication date: November 12, 2020
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
  • Patent number: 10776511
    Abstract: 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: Grant
    Filed: November 7, 2017
    Date of Patent: September 15, 2020
    Assignee: Apple Inc.
    Inventors: Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Patent number: 10726139
    Abstract: 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: Grant
    Filed: September 30, 2017
    Date of Patent: July 28, 2020
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
  • Patent number: 10701042
    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.
    Type: Grant
    Filed: October 12, 2018
    Date of Patent: June 30, 2020
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Publication number: 20200104705
    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.
    Type: Application
    Filed: August 29, 2019
    Publication date: April 2, 2020
    Inventors: Abhishek Bhowmick, Ryan M. Rogers, Umesh S. Vaishampayan, Andrew H. Vyrros
  • Patent number: 10599867
    Abstract: 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: Grant
    Filed: November 7, 2017
    Date of Patent: March 24, 2020
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
  • Patent number: 10599868
    Abstract: 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: Grant
    Filed: November 7, 2017
    Date of Patent: March 24, 2020
    Assignee: Apple Inc.
    Inventors: Gavin Barraclough, Christophe Dumez, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Patent number: 10574770
    Abstract: 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: Grant
    Filed: May 14, 2018
    Date of Patent: February 25, 2020
    Assignee: 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
  • Patent number: 10454962
    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.
    Type: Grant
    Filed: October 12, 2018
    Date of Patent: October 22, 2019
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Publication number: 20190244138
    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.
    Type: Application
    Filed: February 8, 2018
    Publication date: August 8, 2019
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Ryan M. Rogers
  • Publication number: 20190097978
    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.
    Type: Application
    Filed: October 12, 2018
    Publication date: March 28, 2019
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Publication number: 20190089797
    Abstract: 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: Application
    Filed: May 14, 2018
    Publication date: March 21, 2019
    Applicant: 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
  • Publication number: 20190068628
    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.
    Type: Application
    Filed: October 12, 2018
    Publication date: February 28, 2019
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Patent number: 10154054
    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.
    Type: Grant
    Filed: June 30, 2017
    Date of Patent: December 11, 2018
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Publication number: 20180349620
    Abstract: 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: Application
    Filed: September 30, 2017
    Publication date: December 6, 2018
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
  • Publication number: 20180349636
    Abstract: 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: Application
    Filed: November 7, 2017
    Publication date: December 6, 2018
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Publication number: 20180349637
    Abstract: 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: Application
    Filed: November 7, 2017
    Publication date: December 6, 2018
    Inventors: Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Publication number: 20180349638
    Abstract: 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: Application
    Filed: November 7, 2017
    Publication date: December 6, 2018
    Inventors: Gavin Barraclough, Christophe Dumez, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan