Patents by Inventor Julien Freudiger

Julien Freudiger 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: 11256864
    Abstract: Private and secure autocomplete suggestions are enabled based on a user contacts database, even when an application has not been granted access to the user contacts database. A keyboard process can receive and display suggestions based on input provided via the keyboard. The suggestions are generated based on a contacts database of a user. The suggestions are generated without exposing the contacts database to the application. Suggestions are then displayed to the user without exposing the suggestions to the application. Once a suggestion is selected by a user, the selected suggestion is provided to the application for insertion into a text field.
    Type: Grant
    Filed: February 23, 2021
    Date of Patent: February 22, 2022
    Assignee: Apple, Inc.
    Inventors: Zeheng Chen, Jessica Aranda, Patrick Coffman, Patrick W. Demasco, Julien Freudiger, Karan Misra, Stephen J. Rhee, Guy L. Tribble
  • Publication number: 20210400037
    Abstract: An access control system is provided to prevent the surreptitious granting of access to privacy related functionality on an electronic device. Software-based events to grant access to device functionality can be validated by confirming that the software event corresponds with a hardware input event. This validation prevents the spoofing of a user interface input that may be used to fraudulently grant access to specific functionality.
    Type: Application
    Filed: January 29, 2021
    Publication date: December 23, 2021
    Inventors: James R. Montgomerie, Jessica Aranda, Patrick Coffman, Julien Freudiger, Matthew Hansen Gamble, Ron Huang, Anant Jain, Glen S. Low, Andrey Pokrovskiy, Stephen J. Rhee, Matthew E. Shepherd, Ansh Shukla, Katherine Skinner, Kyle Martin Sluder, Christopher Soli, Christopher K. Thomas, Guy L. Tribble, John Wilander
  • Publication number: 20210397751
    Abstract: Embodiments described herein provide a software-based privacy indicator for a camera and microphone that focuses not purely on hardware status (e.g., on or off), but on whether potentially private data is flowing to the system or an application. If based purely on hardware status, the indicator for an electronic device may be shown in scenarios where no data actually flows to the system or applications. The privacy indicator will be enabled if any camera or microphone data is relayed to the operating system or an application that is executed via the operating system. When the device uses the microphone and camera to capture environmental metadata about the surroundings of the device without providing any audio samples, images, or video frames to the system or an application, the privacy indicator will not be enabled.
    Type: Application
    Filed: March 3, 2021
    Publication date: December 23, 2021
    Inventors: Deepak Iyer, Jessica Aranda, Cindy M. Barrett, Patrick Coffman, Julien Freudiger, Alexander S. Haas, Nahir A. Khan, Behkish J. Manzari, Kevin M. Miller, Brian Pietsch, Stephen J. Rhee, Stefan Stuerke, Eric L. Wilson
  • Publication number: 20210397789
    Abstract: Private and secure autocomplete suggestions are enabled based on a user contacts database, even when an application has not been granted access to the user contacts database. A keyboard process can receive and display suggestions based on input provided via the keyboard. The suggestions are generated based on a contacts database of a user. The suggestions are generated without exposing the contacts database to the application. Suggestions are then displayed to the user without exposing the suggestions to the application. Once a suggestion is selected by a user, the selected suggestion is provided to the application for insertion into a text field.
    Type: Application
    Filed: February 23, 2021
    Publication date: December 23, 2021
    Inventors: Zeheng Chen, Jessica Aranda, Patrick Coffman, Patrick W. Demasco, Julien Freudiger, Karan Misra, Stephen J. Rhee, Guy L. Tribble
  • Patent number: 11113289
    Abstract: A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.
    Type: Grant
    Filed: July 12, 2017
    Date of Patent: September 7, 2021
    Assignee: Apple Inc.
    Inventors: Hon Yuk Chan, John M. Hörnkvist, Lun Cui, Vipul Ved Prakash, Anubhav Malhotra, Stanley N. Hung, Julien Freudiger
  • Patent number: 11088834
    Abstract: The current invention provides a system and method for Data Owners to share with Data Seekers extracted insights from the Big Data, instead of raw data or anonymized raw data, thus reducing or eliminating privacy concerns on the data owned by the Data Owners. An Oblivious Pseudo Random Function (OPRF) is used, with operations using OPRFs occur over encrypted data, thus Data Owners learn only the primary object from Data Seeker and nothing else about the remainder of Data Owners' data. Similarly, Data Seeker learns a list of associated secondary objects and nothing else about Data Owners' data. The extent of sharing can be limited using a predefined threshold depending how much private information Data Owner is willing to share or Data Seeker is willing to pay.
    Type: Grant
    Filed: April 28, 2015
    Date of Patent: August 10, 2021
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Julien Freudiger, Shantanu Rane, Alejandro E. Brito, Ersin Uzun
  • 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: 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
  • Patent number: 11003672
    Abstract: A method and apparatus of a device that re-rank a plurality of search results is described. In an exemplary embodiment, the device receives a search query from a user and generates the plurality of search results over a plurality of search domains, wherein the plurality of search results is ranked according to a first ranking. The device additionally generates a re-ranking model, where the re-ranking model includes a plurality of intra-domain models that are generated based on at least based on-device interactions of a plurality of users interacting with a plurality of other devices and each of the plurality of search domains corresponds to one of the plurality of intra-domain models. The device further re-ranks the plurality of search results using the re-ranking model and presents the plurality of search results using the second ranking.
    Type: Grant
    Filed: July 12, 2017
    Date of Patent: May 11, 2021
    Assignee: Apple Inc.
    Inventors: Hon Yuk Chan, John M. Hörnkvist, Lun Cui, Vipul Ved Prakash, Anubhav Malhotra, Stanley N. Hung, Julien Freudiger
  • 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
  • Patent number: 10462107
    Abstract: A computer-implemented system and method for analyzing data quality is provided. Attributes each associated with one or more elements are maintained. A request from a user is received for determining data quality of at least one attribute based on an interest vector having a listing of the elements of that attribute and a selection of elements of interest. Each element is encrypted. A condensed vector having the same listing of elements as the interest vector is populated with occurrence frequencies for each of the listed elements. The elements of the condensed vector are encrypted by computing an encrypted product of each element in the condensed vector and the corresponding element of the interest vector. An aggregate is determined based on the encrypted products of each element of the interest vector and the corresponding element of the condensed vector. The aggregate is provided as results of the data quality.
    Type: Grant
    Filed: August 8, 2016
    Date of Patent: October 29, 2019
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Julien Freudiger, Shantanu Rane, Alejandro E. Brito, Ersin Uzun
  • 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: 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: 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: 10133725
    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: April 3, 2017
    Date of Patent: November 20, 2018
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Publication number: 20180121435
    Abstract: A method and apparatus of a device that re-rank a plurality of search results is described. In an exemplary embodiment, the device receives a search query from a user and generates the plurality of search results over a plurality of search domains, wherein the plurality of search results is ranked according to a first ranking. The device additionally generates a re-ranking model, where the re-ranking model includes a plurality of intra-domain models that are generated based on at least based on-device interactions of a plurality of users interacting with a plurality of other devices and each of the plurality of search domains corresponds to one of the plurality of intra-domain models. The device further re-ranks the plurality of search results using the re-ranking model and presents the plurality of search results using the second ranking.
    Type: Application
    Filed: July 12, 2017
    Publication date: May 3, 2018
    Inventors: Hon Yuk Chan, John M. Hörnkvist, Lun Cui, Vipul Ved Prakash, Anubhav Malhotra, Stanley N. Hung, Julien Freudiger
  • Publication number: 20180121803
    Abstract: A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.
    Type: Application
    Filed: July 12, 2017
    Publication date: May 3, 2018
    Inventors: Hon Yuk Chan, John M. Hörnkvist, Lun Cui, Vipul Ved Prakash, Anubhav Malhotra, Stanley N. Hung, Julien Freudiger
  • Publication number: 20180039619
    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: April 3, 2017
    Publication date: February 8, 2018
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Patent number: 9858426
    Abstract: A computer-implemented system and method for automatically identifying attributes for anonymization is provided. A dataset of attributes is accessed. Each attribute in the dataset is associated with a plurality of values. The values of the dataset are encrypted and the attributes are processed by assigning a sensitivity level to each of the attributes. One or more of the attributes are selected from the dataset for anonymization based on the assigned sensitivity levels.
    Type: Grant
    Filed: November 3, 2015
    Date of Patent: January 2, 2018
    Assignee: PALO ALTO RESEARCH CENTER INCORPORATED
    Inventors: Julien Freudiger, Shantanu Rane, Alejandro E. Brito, Ersin Uzun
  • Patent number: 9842215
    Abstract: A computer-implemented system and method for anonymizing encrypted data is provided. At least one attribute is identified within a dataset for anonymization and is associated with a plurality of data values. Each data value is encrypted for each identified attribute while maintaining an order of the encrypted data values. The encrypted values are ordered and the ordered encrypted data values are segmented into two or more classes based on the ordering of the encrypted data values. A range of the encrypted data values within each of the segmented classes is identified and the range of one of the classes is assigned to each encrypted data value within that class as anonymized data.
    Type: Grant
    Filed: November 3, 2015
    Date of Patent: December 12, 2017
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Julien Freudiger, Alejandro E. Brito, Shantanu Rane, Ersin Uzun