Patents by Inventor Sharathchandra Pankanti

Sharathchandra Pankanti 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: 11321382
    Abstract: A framework is provided in which a querying agency can request (via a query entity) encrypted data through a service provider from a data owning agency that stores encrypted data. The framework uses homomorphic encryption. The data may be gallery entities, and each of the elements in the framework operate on doubly-encrypted information. The service provider compares a representation of an encrypted query entity from the querying agency and representations of encrypted gallery entities from the data owning agency, resulting in doubly-encrypted values of a metric between corresponding compared representations. The querying agency gets result(s), based on the metric, which indicate whether it is probable the service provider has data similar to or the same as query data in the query entity. The elements have to perform communication in order for the querying agency or the data owning agency to get cleartext information corresponding to the query entity.
    Type: Grant
    Filed: February 11, 2020
    Date of Patent: May 3, 2022
    Assignee: International Business Machines Corporation
    Inventors: Sharathchandra Pankanti, Karthik Nandakumar, Nalini K. Ratha, Shai Halevi
  • Patent number: 11301504
    Abstract: A bias compensation method, system, and computer program product include modifying a behavior of a first analytic engine service with a second analytic engine service, where the first service accepts user submitted data and communicates an assessment of the data in a form of a label associated with the corresponding submitted data, where the second service accepts an input and communicates an assessment in a form of a label associated with the corresponding input, and where a behavior model of the first service and the second service includes a discrepancy between the output labels by each service with respect to true labels of data accepted, further including composing a new analytic engine service from the first service and the second service to optimize a service bias in terms of a test dataset based on the behavior model and the known true assessments.
    Type: Grant
    Filed: September 28, 2018
    Date of Patent: April 12, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jonathan Hudson Connell, II, Nalini K. Ratha, Sharathchandra Pankanti
  • Patent number: 11244316
    Abstract: An example operation may include one or more of obtaining a first biometric sample of a user from a user device. extracting, by an issuing node of a permissioned blockchain network, a biometric template from the first biometric sample, encrypting the biometric template, distributing an issuetoken proposal comprising the encrypted biometric template to the blockchain network, and generating and distributing a biometric token to the user device. In response to the user indicating to the user device to redeem the biometric token, the method includes one or more of presenting, by the user device, the biometric token to a verifying node of the blockchain network, validating, by the verifying node, the biometric token, receiving, by the verifying node, a second biometric sample from the user device, distributing a redeemtoken proposal to the blockchain network, committing a transaction corresponding to the biometric token, to the blockchain network, and invalidating the biometric token.
    Type: Grant
    Filed: June 7, 2018
    Date of Patent: February 8, 2022
    Assignee: International Business Machines Corporation
    Inventors: Shelby Solomon Darnell, Karthik Nandakumar, Sharathchandra Pankanti, Nalini K. Ratha
  • Publication number: 20210397988
    Abstract: This disclosure provides a method, apparatus and computer program product to create a full homomorphic encryption (FHE)-friendly machine learning model. The approach herein leverages a knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets. In the approach herein, the distillation framework uses the more complex teacher model to facilitate training of the FHE-friendly model, but using synthetically-generated training data in lieu of the original datasets used to train the teacher.
    Type: Application
    Filed: June 22, 2020
    Publication date: December 23, 2021
    Applicant: International Business Machines Corporation
    Inventors: Kanthi Sarpatwar, Nalini K. Ratha, Karthikeyan Shanmugam, Karthik Nandakumar, Sharathchandra Pankanti, Roman Vaculin, James Thomas Rayfield
  • Publication number: 20210376995
    Abstract: A technique for computationally-efficient privacy-preserving homomorphic inferencing against a decision tree. Inferencing is carried out by a server against encrypted data points provided by a client. Fully homomorphic computation is enabled with respect to the decision tree by intelligently configuring the tree and the real number-valued features that are applied to the tree. To that end, and to the extent the decision tree is unbalanced, the server first balances the tree. A cryptographic packing scheme is then applied to the balanced decision tree and, in particular, to one or more entries in at least one of: an encrypted feature set, and a threshold data set, that are to be used during the decision tree evaluation process. Upon receipt of an encrypted data point, homomorphic inferencing on the configured decision tree is performed using a highly-accurate approximation comparator, which implements a “soft” membership recursive computation on real numbers, all in an oblivious manner.
    Type: Application
    Filed: May 27, 2020
    Publication date: December 2, 2021
    Applicant: International Business Machines Corporation
    Inventors: Nalini K. Ratha, Kanthi Sarpatwar, Karthikeyan Shanmugam, Sharathchandra Pankanti, Karthik Nandakumar, Roman Vaculin
  • Patent number: 11188798
    Abstract: Multiple trained AI models are tested using known genuine samples of respective multiple modalities of multimedia to generate versions of the multiple modalities of a given multimedia sample. Data for the multimedia and the multimedia sample are divided into the multiple modalities. Respective differences are computed between respective components of the multiple trained AI models to produce respective multiple difference vector, which are compared with corresponding baseline difference vectors determined in order to train the multiple trained AI models. The given multimedia sample is classified as genuine or altered using at least the comparison.
    Type: Grant
    Filed: May 21, 2020
    Date of Patent: November 30, 2021
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210365714
    Abstract: Multiple trained AI models are tested using known genuine samples of respective multiple modalities of multimedia to generate versions of the multiple modalities of a given multimedia sample. Data for the multimedia and the multimedia sample are divided into the multiple modalities. Respective differences are computed between respective components of the multiple trained AI models to produce respective multiple difference vector, which are compared with corresponding baseline difference vectors determined in order to train the multiple trained AI models. The given multimedia sample is classified as genuine or altered using at least the comparison.
    Type: Application
    Filed: May 21, 2020
    Publication date: November 25, 2021
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210344478
    Abstract: A method, apparatus and computer program product for homomorphic inference on a decision tree (DT) model. In lieu of HE-based inferencing on the decision tree, the inferencing instead is performed on a neural network (NN), which acts as a surrogate. To this end, the neural network is trained to learn DT decision boundaries, preferably without using the original DT model data training points. During training, a random data set is applied to the DT, and expected outputs are recorded. This random data set and the expected outputs are then used to train the neural network such that the outputs of the neural network match the outputs expected from applying the original data set to the DT. Preferably, the neural network has low depth, just a few layers. HE-based inferencing on the decision tree is done using HE inferencing on the shallow neural network. The latter is computationally-efficient and is carried without the need for bootstrapping.
    Type: Application
    Filed: April 30, 2020
    Publication date: November 4, 2021
    Applicant: International Business Machines Corporation
    Inventors: Kanthi Sarpatwar, Nalini K. Ratha, Karthikeyan Shanmugam, Karthik Nandakumar, Sharathchandra Pankanti, Roman Vaculin
  • Publication number: 20210322273
    Abstract: A drug delivery device includes a capsule, a logic circuit disposed within the capsule, and an actuator connected to the logic circuit and configured to expose an interior of the capsule to an exterior of the capsule upon activation by the logic circuit.
    Type: Application
    Filed: June 29, 2021
    Publication date: October 21, 2021
    Inventors: NOEL C. CODELLA, JONATHAN H. CONNELL, II, SHARATHCHANDRA PANKANTI, NALINI K. RATHA
  • Patent number: 11151236
    Abstract: An example operation may include one or more of initiating, by a file verification device, verification of a source file or a redacted source file, executing one of a smart contract or chaincode to verify the chameleon hash signature and the auxiliary data hash signature, and providing a notification whether the verification was successful or unsuccessful. In response to initiating verification of the source file, the method further includes the file verification device receiving stored source file segments and stored auxiliary data segments, generating a chameleon hash signature, and generating an auxiliary data hash signature. In response to initiating verification of the redacted source file, the method further includes receiving stored redacted file segments, stored auxiliary data segments, and stored modified auxiliary data, generating a chameleon hash signature, and generating an auxiliary data hash signature.
    Type: Grant
    Filed: December 20, 2018
    Date of Patent: October 19, 2021
    Assignee: International Business Machines Corporation
    Inventors: Karthik Nandakumar, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210312677
    Abstract: Methods and systems for generating a composite image in remote sensing applications are described. In an example, a device can receive an image having a plurality of points specified in an image space. The device can extract a portion of the image and transform points among the extracted portion from the image space to a parameter space defined by a distance parameter and an orientation parameter. The device can identify a set of intersection points in the parameter space that indicate at least one occurrence of a geometry feature in the extracted portion of the image. The device can augment the portion of the image with a plurality of new pixels based on the identified set of intersection points. The device can generate a composite image using the augmented image, where the composite image can include a plurality of augmented images corresponding to other portions of the image.
    Type: Application
    Filed: April 1, 2020
    Publication date: October 7, 2021
    Inventors: Conrad M. Albrecht, Marcus Oliver Freitag, Sharathchandra Pankanti, Siyuan Lu, Hendrik F. Hamann
  • Patent number: 11139960
    Abstract: An example operation may include one or more of determining, by a file redaction device, redacted segments of a source file, receiving, by a signature update device, the redacted source file segments, a stored trapdoor key, and stored auxiliary data segments, determining modified auxiliary data from the redacted source file segments, the trapdoor key and the auxiliary data segments, executing chaincode to obtain a modified auxiliary data signature and identifiers of the redacted source file segments, and storing the modified auxiliary data signature and identifiers of the redacted source file segments to a shared ledger of a blockchain network. Each stored auxiliary data segment including a random string of data corresponding to a segment of the source file.
    Type: Grant
    Filed: December 20, 2018
    Date of Patent: October 5, 2021
    Assignee: International Business Machines Corporation
    Inventors: Karthik Nandakumar, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210264268
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Application
    Filed: April 21, 2021
    Publication date: August 26, 2021
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210248176
    Abstract: A framework is provided in which a querying agency can request (via a query entity) encrypted data through a service provider from a data owning agency that stores encrypted data. The framework uses homomorphic encryption. The data may be gallery entities, and each of the elements in the framework operate on doubly-encrypted information. The service provider compares a representation of an encrypted query entity from the querying agency and representations of encrypted gallery entities from the data owning agency, resulting in doubly-encrypted values of a metric between corresponding compared representations. The querying agency gets result(s), based on the metric, which indicate whether it is probable the service provider has data similar to or the same as query data in the query entity. The elements have to perform communication in order for the querying agency or the data owning agency to get cleartext information corresponding to the query entity.
    Type: Application
    Filed: February 11, 2020
    Publication date: August 12, 2021
    Inventors: Sharathchandra Pankanti, Karthik Nandakumar, Nalini K. Ratha, Shai Halevi
  • Patent number: 11087223
    Abstract: A computer system receives a set of data encrypted by a homomorphic encryption transformation. The computer system performs machine learning operations using the encrypted set of data. The machine learning operations build, using homomorphic operations, a trained model of the data having a mapping between the encrypted data and output of the trained model. The model is stored for use for performing inferencing of other encrypted data to determine a corresponding output of the trained model. The computer system may perform inferencing of the other encrypted data at least by accessing the stored trained model and predicting by using the trained model a label in an encrypted format that corresponds to the other encrypted data. The computer system may send the label toward the client for the client to decrypt the label.
    Type: Grant
    Filed: July 11, 2018
    Date of Patent: August 10, 2021
    Assignee: International Business Machines Corporation
    Inventors: Karthik Nandakumar, Nalini K. Ratha, Shai Halevi, Sharathchandra Pankanti
  • Patent number: 11052023
    Abstract: A drug delivery device includes a capsule, a logic circuit disposed within the capsule, and an actuator connected to the logic circuit and configured to expose an interior of the capsule to an exterior of the capsule upon activation by the logic circuit.
    Type: Grant
    Filed: December 20, 2017
    Date of Patent: July 6, 2021
    Assignee: International Business Machines Corporation
    Inventors: Noel C. Codella, Jonathan H. Connell, II, Sharathchandra Pankanti, Nalini K. Ratha
  • Publication number: 20210200218
    Abstract: In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.
    Type: Application
    Filed: December 26, 2019
    Publication date: July 1, 2021
    Inventors: Conrad M. ALBRECHT, Ildar KHABIBRAKHMANOV, Sharathchandra PANKANTI, Levente KLEIN, Wang ZHOU, Bruce Gordon ELMEGREEN, Siyuan LU, Hendrick F. HAMANN, Carlo SIEBENSCHUH
  • Patent number: 11042799
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Grant
    Filed: August 20, 2019
    Date of Patent: June 22, 2021
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Patent number: 11025430
    Abstract: An example operation may include one or more of creating a source file, segmenting the source file into source file segments, creating a number of auxiliary data segments corresponding to source file segments, performing a chameleon hash of the source file segments and the auxiliary data segments, obtaining a source file signature from the chameleon hash, performing a cryptographic hash of the auxiliary data segments, obtaining an auxiliary data signature from the cryptographic hash, and storing the source file and cryptographic signatures to a shared ledger of a blockchain network. Each auxiliary data segment includes a random string of data that is generated based on a corresponding source file segment.
    Type: Grant
    Filed: December 20, 2018
    Date of Patent: June 1, 2021
    Assignee: International Business Machines Corporation
    Inventors: Karthik Nandakumar, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210118117
    Abstract: Methods and systems for managing vegetation include training a machine learning model based on an image of a training data region before a weather event, an image of the training data region after the weather event, and information regarding the weather event. A risk score is generated for a second region using the trained machine learning model based on an image of the second region and predicted weather information for the second region. The risk score is determined to indicate high-risk vegetation in the second region. A corrective action is performed to reduce the risk of vegetation in the second region.
    Type: Application
    Filed: October 21, 2019
    Publication date: April 22, 2021
    Inventors: Conrad M. Albrecht, Hendrik F. Hamann, Levente Klein, Siyuan Lu, Sharathchandra Pankanti, Wang Zhou