Patents by Inventor Kieron Francois Pascal GUINAMARD

Kieron Francois Pascal GUINAMARD 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: 20230359770
    Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.
    Type: Application
    Filed: July 10, 2023
    Publication date: November 9, 2023
    Inventors: Jason Derek MCFALL, Charles Codman CABOT, Timothy James MORAN, Kieron Francois Pascal GUINAMARD, Vladimir Michael EATWELL, Benjamin Thomas PICKERING, Paul David MELLOR, Theresa STADLER, Andrei PETRE, Christopher Andrew SMITH, Anthony Jason DU PREEZ, Igor VUJOSEVIC, George DANEZIS
  • Patent number: 11698990
    Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.
    Type: Grant
    Filed: May 2, 2017
    Date of Patent: July 11, 2023
    Assignee: PRIVITAR LIMITED
    Inventors: Jason Derek McFall, Charles Codman Cabot, Timothy James Moran, Kieron Francois Pascal Guinamard, Vladimir Michael Eatwell, Benjamin Thomas Pickering, Paul David Mellor, Theresa Stadler, Andrei Petre, Christopher Andrew Smith, Anthony Jason Du Preez, Igor Vujosevic, George Danezis
  • Publication number: 20220277097
    Abstract: A computer implemented method is presented for querying a dataset that contains sensitive attributes. The method comprises the steps of receiving a query specification, generating a set of aggregate statistics derived from the sensitive dataset based on the query specification and encoding the set of aggregate statistics using a set of linear equations. The relationships of each sensitive attribute represented in the set of aggregate statistics are also encoded into the set of linear equations.
    Type: Application
    Filed: June 12, 2020
    Publication date: September 1, 2022
    Inventors: Charles Codman CABOT, Kieron Francois Pascal GUINAMARD, Jason Derek MCFALL, Pierre-Andre MAUGIS, Hector PAGE, Benjamin Thomas PICKERING, Theresa STADLER, Jo-anne TAY, Suzanne WELLER
  • Publication number: 20210012028
    Abstract: A computer implemented data product release method or system. The data product release is derived from a sensitive dataset using a privacy protection system such as a differentially private system. The privacy protection parameters, such as noise addition magnitude, are configurable as part of the data product release method or system to alter the balance between maintaining privacy of the sensitive dataset and making the data product release useful.
    Type: Application
    Filed: December 18, 2018
    Publication date: January 14, 2021
    Applicant: Privitar Limited
    Inventors: Charles Codman CABOT, Kieron Francois Pascal GUINAMARD, Jason Derek MCFALL, Pierre-Andre MAUGIS, Hector PAGE, Benjamin Thomas PICKERING, Theresa STADLER, Jo-anne TAY, Suzanne WELLER
  • Publication number: 20200327252
    Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.
    Type: Application
    Filed: May 2, 2017
    Publication date: October 15, 2020
    Inventors: Jason Derek MCFALL, Charles Codman CABOT, Timothy James MORAN, Kieron Francois Pascal GUINAMARD, Vladimir Michael EATWELL, Benjamin Thomas PICKERING, Paul David MELLOR, Theresa STADLER, Andrei PETRE, Christopher Andrew SMITH, Anthony Jason DU PREEZ, Igor VUJOSEVIC, George DANEZIS