Patents by Inventor Venkata Sitaramagiridharganesh Ganapavarapu

Venkata Sitaramagiridharganesh Ganapavarapu 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: 11475365
    Abstract: An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.
    Type: Grant
    Filed: April 9, 2020
    Date of Patent: October 18, 2022
    Assignee: International Business Machines Corporation
    Inventors: Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Roman Vaculin
  • Patent number: 11356275
    Abstract: A method verifies an authenticity, integrity, and provenance of outputs from steps in a process flow. One or more processor(s) validate one or more inputs to each step in a process flow by verifying at least one of a hash and a digital signature of each of the one or more inputs. The processor(s) then generate digital signatures that cover outputs of each step and the one or more inputs to each step, such that the digital signatures result in a chain of digital signatures that are used to verify an authenticity, an integrity and a provenance of outputs of the one or more steps in the process flow.
    Type: Grant
    Filed: May 27, 2020
    Date of Patent: June 7, 2022
    Assignee: International Business Machines Corporation
    Inventors: Enriquillo Valdez, Richard H. Boivie, Venkata Sitaramagiridharganesh Ganapavarapu, Jinwook Jung, Gi-Joon Nam, Roman Vaculin, James Thomas Rayfield
  • Patent number: 11271958
    Abstract: Aspects of the present disclosure describe techniques for detecting anomalous data in an encrypted data set. An example method generally includes receiving a data set of encrypted data points. A tree data structure having a number of levels is generated for the data set. Each level of the tree data structure generally corresponds to a feature of the encrypted plurality of features, and each node in the tree data structure at a given level represents a probability distribution of a likelihood that each data point is less than or greater than a split value determined for a given feature. An encrypted data point is received for analysis, and anomaly score is calculated based on a probability identified for each of the plurality of encrypted features. Based on determining that the calculated anomaly score exceeds a threshold value, the encrypted data point is identified as potentially anomalous.
    Type: Grant
    Filed: September 20, 2019
    Date of Patent: March 8, 2022
    Assignee: International Business Machines Corporation
    Inventors: Kanthi Sarpatwar, Venkata Sitaramagiridharganesh Ganapavarapu, Saket Sathe, Roman Vaculin
  • Publication number: 20210377042
    Abstract: A method verifies an authenticity, integrity, and provenance of outputs from steps in a process flow. One or more processor(s) validate one or more inputs to each step in a process flow by verifying at least one of a hash and a digital signature of each of the one or more inputs. The processor(s) then generate digital signatures that cover outputs of each step and the one or more inputs to each step, such that the digital signatures result in a chain of digital signatures that are used to verify an authenticity, an integrity and a provenance of outputs of the one or more steps in the process flow.
    Type: Application
    Filed: May 27, 2020
    Publication date: December 2, 2021
    Inventors: ENRIQUILLO VALDEZ, RICHARD H. BOIVIE, VENKATA SITARAMAGIRIDHARGANESH GANAPAVARAPU, JINWOOK JUNG, GI-JOON NAM, ROMAN VACULIN, JAMES THOMAS RAYFIELD
  • Publication number: 20210319353
    Abstract: An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.
    Type: Application
    Filed: April 9, 2020
    Publication date: October 14, 2021
    Inventors: Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Roman Vaculin
  • Publication number: 20210092137
    Abstract: Aspects of the present disclosure describe techniques for detecting anomalous data in an encrypted data set. An example method generally includes receiving a data set of encrypted data points. A tree data structure having a number of levels is generated for the data set. Each level of the tree data structure generally corresponds to a feature of the encrypted plurality of features, and each node in the tree data structure at a given level represents a probability distribution of a likelihood that each data point is less than or greater than a split value determined for a given feature. An encrypted data point is received for analysis, and anomaly score is calculated based on a probability identified for each of the plurality of encrypted features. Based on determining that the calculated anomaly score exceeds a threshold value, the encrypted data point is identified as potentially anomalous.
    Type: Application
    Filed: September 20, 2019
    Publication date: March 25, 2021
    Inventors: Kanthi Sarpatwar, Venkata Sitaramagiridharganesh Ganapavarapu, Saket Sathe, Roman Vaculin
  • Publication number: 20200394470
    Abstract: An example operation may include one or more of generating, by a training participant client, a plurality of transaction proposals, each of the plurality of transaction proposals corresponding to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, transferring the plurality of transaction proposals to one or more endorser nodes or peers each comprising a verify gradient smart contract, executing, by each of the endorser nodes or peers, the verify gradient smart contract; and providing endorsements corresponding to the plurality of transaction proposals to the training participation client.
    Type: Application
    Filed: June 12, 2019
    Publication date: December 17, 2020
    Inventors: Venkata Sitaramagiridharganesh Ganapavarapu, Kanthi Sarpatwar, Karthikeyan Shanmugam, Roman Vaculin
  • Publication number: 20200394552
    Abstract: An example operation may include one or more of generating, by a plurality of training participant clients, gradient calculations for machine learning model training, each of the training participant clients comprising a training dataset, converting, by a training aggregator coupled to the plurality of training participant clients, the gradient calculations to a plurality of transaction proposals, receiving, by one or more endorser nodes or peers of a blockchain network, the plurality of transaction proposals, executing, by each of the endorser nodes or peers, a verify gradient smart contract, and providing endorsements corresponding to the plurality of transaction proposals to the training aggregator.
    Type: Application
    Filed: June 12, 2019
    Publication date: December 17, 2020
    Inventors: Venkata Sitaramagiridharganesh Ganapavarapu, Kanthi Sarpatwar, Karthikeyan Shanmugam, Roman Vaculin
  • Publication number: 20200394471
    Abstract: An example operation may include one or more of generating, by a training participant client comprising a training dataset, a plurality of transaction proposals that each correspond to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, a batch from the private dataset, a loss function, and an original model parameter, receiving, by one or more endorser nodes of peers of a blockchain network, the plurality of transaction proposals, and evaluating each transaction proposal.
    Type: Application
    Filed: June 12, 2019
    Publication date: December 17, 2020
    Inventors: Venkata Sitaramagiridharganesh Ganapavarapu, Kanthi Sarpatwar, Karthikeyan Shanmugam, Roman Vaculin