Patents by Inventor Ariel Farkash

Ariel Farkash 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: 11528134
    Abstract: An example system includes a processor to receive, at a setup or sign-up, a first cipher including a biometric template transformed using a first transformation and encrypted using a secret key, a second cipher including a security vector encrypted using the secret key, a third cipher including the biometric template transformed using a second transformation and encrypted, and a fourth cipher including an encrypted second security vector. The processor can receive, at a runtime or sign-in, a fifth cipher and a sixth cipher. The processor can verify that the fifth cipher includes a second biometric template transformed using the first transformation and encrypted using the secret key and that the sixth cipher includes the second biometric template transformed using the second transformation by testing a format attribute of the transformation functions using comparisons of inner products.
    Type: Grant
    Filed: March 24, 2020
    Date of Patent: December 13, 2022
    Assignee: International Business Machines Corporation
    Inventors: Ehud Aharoni, Allon Adir, Dov Murik, Ariel Farkash, Omri Soceanu
  • Publication number: 20220391529
    Abstract: An example system includes a processor to generate regular expressions representing textual pattern facets of sub-formats of a composite format, and a regular expression representing a composite textual pattern of the composite format based on sub-format and composition type. The processor can search the data using generated regular expression representing composite textual patterns to detect occurrences of candidate matches. The processor can recursively match and validate the detected occurrences with the composite format and hierarchically match and validate sub-formats in the detected occurrence. The processor can mask in place the detected occurrence of the composite format in the data using ranking-based integer format preserving masking.
    Type: Application
    Filed: June 1, 2021
    Publication date: December 8, 2022
    Inventors: Ariel FARKASH, Micha Gideon MOFFIE
  • Publication number: 20220300837
    Abstract: A method, computer system, and a computer program product for testing a data removal are provided. Data elements are marked with a respective mark per represented entity. The marked data elements, with labels indicating the respective marks, are input into a machine learning model to form a trained machine learning model. The trained machine learning model is configured to perform a dual task that includes a main task and a secondary task that includes a classification based on the labels. A forgetting mechanism is applied to the trained machine learning model to remove a data element including a test mark of the marked data elements. A test data element marked with the test mark is input into the revised machine learning model. The classification of the secondary task of an output of the revised machine learning model is determined for the input test data element.
    Type: Application
    Filed: March 22, 2021
    Publication date: September 22, 2022
    Inventors: RON SHMELKIN, Abigail Goldsteen, GILAD EZOV, ARIEL FARKASH
  • Publication number: 20220300822
    Abstract: A method for forgetting data samples from a pretrained neural network (NN) model is provided. The method includes training an adversarial model to classify training data samples as members of the NN model and test data samples as non-members of the NN model. The method includes performing the following iteratively until the NN model has forgotten a specified threshold of data samples to be forgotten: (1) classifying the data samples as members or non-members using the trained adversarial model; (2) for the member data samples, determining a subset that includes data samples to be forgotten; (3) labeling the data samples within the subset as non-members and updating the NN model based on weight update techniques that cause the NN model to forget the data samples; (4) retraining the NN model without the data samples that have been forgotten; and (5) retraining the adversarial model for the next iteration.
    Type: Application
    Filed: March 17, 2021
    Publication date: September 22, 2022
    Inventors: Ron SHMELKIN, Abigail GOLDSTEEN, Ariel FARKASH
  • Publication number: 20220269814
    Abstract: A method, computer system, and a computer program product for personal data discovery is provided. The present invention may include determining at least one feature used to train a target machine learning (ML) model. The present invention may also include mapping the determined at least one feature to at least one location of a data store including at least one personal data associated with the determined at least one feature. The present invention may further include retrieving a data record of the at least one personal data associated with the mapped at least one feature from the at least one location of the data store. The present invention may also include determining that the target ML model includes a trace of the retrieved data record. The present invention may further include marking the target ML model as containing the at least one personal data.
    Type: Application
    Filed: February 23, 2021
    Publication date: August 25, 2022
    Inventors: Abigail Goldsteen, Micha Gideon Moffie, ARIEL FARKASH
  • Patent number: 11424928
    Abstract: Embodiments may include techniques to prevent illegal ciphertexts using distance computations on homomorphic and/or functional encrypted templates while detecting whether the resulting distance does not meet requirements for validity.
    Type: Grant
    Filed: May 30, 2020
    Date of Patent: August 23, 2022
    Assignee: International Business Machines Corporation
    Inventors: Ehud Aharoni, Omri Soceanu, Ariel Farkash, Allon Adir
  • Patent number: 11418319
    Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.
    Type: Grant
    Filed: April 30, 2020
    Date of Patent: August 16, 2022
    Assignee: International Business Machines Corporation
    Inventors: Ehud Aharoni, Allon Adir, Lev Greenberg, Omri Soceanu, Ariel Farkash
  • Publication number: 20220253554
    Abstract: An example system includes a processor to receive training data and predictions on the training data of a trained machine learning model to be anonymized. The processor is to generate generalized data from training data based on the predictions of the trained machine learning model on the training data. The processor is to train an anonymized machine learning model using the generalized data.
    Type: Application
    Filed: February 11, 2021
    Publication date: August 11, 2022
    Inventors: Abigail GOLDSTEEN, Ariel FARKASH, Micha Gideon MOFFIE, Gilad EZOV, Ron SHMELKIN
  • Patent number: 11281728
    Abstract: A method, apparatus and a product for data generalization for predictive models. The method comprising: based on a labeled dataset, determining a plurality of buckets, each of which has an associated label; determining a plurality of clusters, grouping similar instances in the same bucket; based on the plurality of clusters, determining an alternative set of features comprising a set of generalized features, wherein each generalized feature corresponds to a cluster of the plurality of clusters, wherein a generalized feature that corresponds to a cluster is indicative of the instance being mapped to the corresponding cluster; obtaining a second instance; determining a generalized second instance that comprises a valuation of the alternative set of features for the second instance; and based on the generalized second instance, determining a label for the second instance.
    Type: Grant
    Filed: August 6, 2019
    Date of Patent: March 22, 2022
    Assignee: International Business Machines Corporation
    Inventors: Gilad Ezov, Ariel Farkash, Abigail Goldsteen, Ron Shmelkin, Micha Gideon Moffie
  • Patent number: 11240044
    Abstract: Embodiments of the present systems and methods may provide techniques for verifying the correct application purpose for applications that serve multiple purposes and to determine the correct purpose for each requested data access. For example, in an embodiment, a method for controlling application access to data implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor may comprise: receiving an application comprising a plurality of application parts, each application part associated with a declared data access purpose and generating a cryptographic certificate for each application part to be certified by determining whether a declared data access purpose for each application part to be certified is correct and the only data access purpose for that part, wherein the declared purpose is included in purpose information associated with each application part to be certified.
    Type: Grant
    Filed: November 22, 2018
    Date of Patent: February 1, 2022
    Assignee: International Business Machines Corporation
    Inventors: Ariel Farkash, Abigail Goldsteen, Micha Gideon Moffie
  • Patent number: 11201745
    Abstract: Embodiments of the present systems and methods may provide encrypted biometric information that can be stored and used for authentication with undegraded recognition performance. For example, in an embodiment, a method may comprise storing a plurality of encrypted trained weights of a neural network classifier, wherein the weights have been trained using biometric information representing at least one biometric feature of a person, receiving encrypted biometric information obtained by sampling at least one biometric feature of the person and encrypting the sampled biometric feature, obtaining an match-score using the encrypted trained neural network classifier, the match-score indicating a probability that the received encrypted biometric information matches the stored encrypted biometric information, and authenticating the person when the probability that received encrypted biometric information matches the stored encrypted biometric information exceeds a threshold.
    Type: Grant
    Filed: January 10, 2019
    Date of Patent: December 14, 2021
    Assignee: International Business Machines Corporation
    Inventors: Muhammad Barham, Ariel Farkash, Ron Shmelkin, Omri Soceanu, Michael Vinov
  • Patent number: 11194841
    Abstract: Automated classification, by: Obtaining an examined document having an examined value appearing therein. Identifying: a location in the examined document at which the examined value appears, and a structure of the examined value. Identifying additional documents of a same type as the examined document, in which values having a same structure as the examined value appear at a same location as in the examined document. Applying a classifier to the examined value and the values in the additional documents, to output a single class to which the examined value and the values in the additional documents belong.
    Type: Grant
    Filed: November 28, 2019
    Date of Patent: December 7, 2021
    Assignee: International Business Machines Corporation
    Inventors: Sigal Asaf, Ariel Farkash, Micha Gideon Moffie
  • Publication number: 20210377031
    Abstract: Embodiments may include techniques to prevent illegal ciphertexts using distance computations on homomorphic and/or functional encrypted templates while detecting whether the resulting distance does not meet requirements for validity.
    Type: Application
    Filed: May 30, 2020
    Publication date: December 2, 2021
    Inventors: Ehud Aharoni, Omri Soceanu, Ariel Farkash, Allon Adir
  • Publication number: 20210344477
    Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.
    Type: Application
    Filed: April 30, 2020
    Publication date: November 4, 2021
    Inventors: Ehud Aharoni, Allon Adir, LEV GREENBERG, OMRI SOCEANU, ARIEL FARKASH
  • Publication number: 20210306147
    Abstract: An example system includes a processor to receive, at a setup or sign-up, a first cipher including a biometric template transformed using a first transformation and encrypted using a secret key, a second cipher including a security vector encrypted using the secret key, a third cipher including the biometric template transformed using a second transformation and encrypted, and a fourth cipher including an encrypted second security vector. The processor can receive, at a runtime or sign-in, a fifth cipher and a sixth cipher. The processor can verify that the fifth cipher includes a second biometric template transformed using the first transformation and encrypted using the secret key and that the sixth cipher includes the second biometric template transformed using the second transformation by testing a format attribute of the transformation functions using comparisons of inner products.
    Type: Application
    Filed: March 24, 2020
    Publication date: September 30, 2021
    Inventors: Ehud Aharoni, Allon Adir, Dov Murik, Ariel Farkash, Omri Soceanu
  • Publication number: 20210165807
    Abstract: Automated classification, by: Obtaining an examined document having an examined value appearing therein. Identifying: a location in the examined document at which the examined value appears, and a structure of the examined value. Identifying additional documents of a same type as the examined document, in which values having a same structure as the examined value appear at a same location as in the examined document. Applying a classifier to the examined value and the values in the additional documents, to output a single class to which the examined value and the values in the additional documents belong.
    Type: Application
    Filed: November 28, 2019
    Publication date: June 3, 2021
    Inventors: Sigal Asaf, ARIEL FARKASH, Micha Gideon Moffie
  • Publication number: 20210042356
    Abstract: A method, apparatus and a product for data generalization for predictive models. The method comprising: based on a labeled dataset, determining a plurality of buckets, each of which has an associated label; determining a plurality of clusters, grouping similar instances in the same bucket; based on the plurality of clusters, determining an alternative set of features comprising a set of generalized features, wherein each generalized feature corresponds to a cluster of the plurality of clusters, wherein a generalized feature that corresponds to a cluster is indicative of the instance being mapped to the corresponding cluster; obtaining a second instance; determining a generalized second instance that comprises a valuation of the alternative set of features for the second instance; and based on the generalized second instance, determining a label for the second instance.
    Type: Application
    Filed: August 6, 2019
    Publication date: February 11, 2021
    Inventors: GILAD EZOV, ARIEL FARKASH, Abigail Goldsteen, RON SHMELKIN, Micha Gideon Moffie
  • Publication number: 20210042629
    Abstract: A method, apparatus and a product for data generalization for predictive models. The method comprising: obtaining a training dataset that comprises a plurality of training instances and predicted labels thereof, wherein each training instance is a valuation of a set of features, wherein the set of features comprises a feature having a domain, wherein the predicted label of each training instance is a label predicted thereto by a predictive model; training an auxiliary model using the training dataset; based on the auxiliary model, determining an alternative set of features that is a generalization of the set of features, wherein the alternative set of features comprises a generalized feature having a generalized domain, wherein each value in the generalized domain corresponds to one or more values in the domain; obtaining a generalized instance having a valuation of the alternative set of features; and determining a label for the generalized instance.
    Type: Application
    Filed: August 6, 2019
    Publication date: February 11, 2021
    Inventors: GILAD EZOV, ARIEL FARKASH, Abigail Goldsteen, RON SHMELKIN, Micha Gideon Moffie
  • Publication number: 20210004637
    Abstract: Embodiments of the present systems and methods may provide techniques to distinguish between data categories. For example, a method implemented in a computer system may comprise obtaining, at the computer system, a plurality of data strings in different categories, each category having a same string pattern, determining a loose string format and a set of restrictions based on at least one string pattern, classifying the plurality of data strings to respective different categories based on a loose string format of the data strings and on the restrictions on the data strings of the different categories using a classification score indicating utilizing restriction information of other categories when determining the matching of a category, and decreasing the classification score if a mean restriction matching proportion is not part of a category or is a threshold amount above an expected mean restriction matching proportion.
    Type: Application
    Filed: July 7, 2019
    Publication date: January 7, 2021
    Inventors: SIGAL ASAF, ARIEL FARKASH, LEV GREENBERG, Micha Gideon Moffie
  • Patent number: 10831869
    Abstract: Embodiments of the present systems and methods may provide data watermarking without reliance on error-tolerant fields, thereby providing for the incorporation of watermarks in data that was not considered suitable for watermarking. For example, in an embodiment, a computer-implemented method for watermarking data may comprise inserting watermark data into a field that requires format-preserving encryption.
    Type: Grant
    Filed: July 2, 2018
    Date of Patent: November 10, 2020
    Assignee: International Business Machines Corporation
    Inventors: Abigail Goldsteen, Lev Greenberg, Ariel Farkash, Boris Rozenberg, Omri Soceanu