Patents by Inventor Chayanika Pragya

Chayanika Pragya 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: 9135320
    Abstract: A system and method for data anonymization using hierarchical data clustering and perturbation is provided. The system includes a computer system and an anonymization program executed by the computer system. The system converts the data of a high-dimensional dataset to a normalized vector space and applies clustering and perturbation techniques to anonymize the data. The conversion results in each record of the dataset being converted into a normalized vector that can be compared to other vectors. The vectors are divided into disjointed, small-sized clusters using hierarchical clustering processes. Multi-level clustering can be performed using suitable algorithms at different clustering levels. The records within each cluster are then perturbed such that the statistical properties of the clusters remain unchanged.
    Type: Grant
    Filed: June 13, 2013
    Date of Patent: September 15, 2015
    Assignee: Opera Solutions, LLC
    Inventors: Kanav Goyal, Chayanika Pragya, Rahul Garg
  • Publication number: 20130339359
    Abstract: A system and method for data anonymization using hierarchical data clustering and perturbation is provided. The system includes a computer system and an anonymization program executed by the computer system. The system converts the data of a high-dimensional dataset to a normalized vector space and applies clustering and perturbation techniques to anonymize the data. The conversion results in each record of the dataset being converted into a normalized vector that can be compared to other vectors. The vectors are divided into disjointed, small-sized clusters using hierarchical clustering processes. Multi-level clustering can be performed using suitable algorithms at different clustering levels. The records within each cluster are then perturbed such that the statistical properties of the clusters remain unchanged.
    Type: Application
    Filed: June 13, 2013
    Publication date: December 19, 2013
    Inventors: Kanav Goyal, Chayanika Pragya, Rahul Garg