Patents by Inventor Abhishek Bhowmick

Abhishek Bhowmick 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).

  • Publication number: 20220067075
    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.
    Type: Application
    Filed: August 10, 2021
    Publication date: March 3, 2022
    Inventors: Mona CHITNIS, Abhishek BHOWMICK, Lucas O. WINSTROM, Koray MANCUHAN, Stephen D. FLEISCHER
  • 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
  • Patent number: 11086915
    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.
    Type: Grant
    Filed: December 9, 2019
    Date of Patent: August 10, 2021
    Assignee: Apple Inc.
    Inventors: Mona Chitnis, Abhishek Bhowmick, Lucas O. Winstrom, Koray Mancuhan, Stephen D. Fleischer
  • Patent number: 11055492
    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.
    Type: Grant
    Filed: February 8, 2019
    Date of Patent: July 6, 2021
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Ryan M. Rogers, Umesh S. Vaishampayan, Kartik R. Venkatraman
  • Publication number: 20210192078
    Abstract: 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.
    Type: Application
    Filed: December 21, 2020
    Publication date: June 24, 2021
    Inventors: 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
  • Publication number: 20210173856
    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.
    Type: Application
    Filed: December 9, 2019
    Publication date: June 10, 2021
    Inventors: Mona CHITNIS, Abhishek BHOWMICK, Lucas O. WINSTROM, Koray MANCUHAN, Stephen D. FLEISCHER
  • Publication number: 20210166157
    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.
    Type: Application
    Filed: January 17, 2020
    Publication date: June 3, 2021
    Inventors: Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, Ryan M. Rogers
  • 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
  • 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
  • Publication number: 20190370334
    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.
    Type: Application
    Filed: February 8, 2019
    Publication date: December 5, 2019
    Inventors: ABHISHEK BHOWMICK, RYAN M. ROGERS, UMESH S. VAISHAMPAYAN, KARTIK R. VENKATRAMAN
  • Publication number: 20190370009
    Abstract: One embodiment provides for a method comprising determining a set of probabilities associated with a set of applications configured to execute on the electronic device, the set of probabilities including a probability of application usage within a period of time, updating a probability model based on the set of probabilities associated with the set of applications, selecting an application to swap to a fatigable storage device based on output from the probability model, and swapping the application to the fatigable storage device, wherein swapping the application includes storing a memory address space for the application and an application state to the fatigable storage device.
    Type: Application
    Filed: April 30, 2019
    Publication date: December 5, 2019
    Inventors: Kartik R. Venkatraman, Abhishek Bhowmick, Lionel D. Desai
  • 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: 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
  • 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: 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