Patents by Inventor Omri Soceanu
Omri Soceanu 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).
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Patent number: 12355859Abstract: An example system includes a processor to compute a tensor of indicators indicating a presence of partial sums in an encrypted vector of indicators. The processor can also securely reorder an encrypted array based on the computed tensor of indicators to generate a reordered encrypted array.Type: GrantFiled: August 25, 2022Date of Patent: July 8, 2025Assignee: International Business Machines CorporationInventors: Eyal Kushnir, Hayim Shaul, Omri Soceanu, Ehud Aharoni, Nathalie Baracaldo Angel, Runhua Xu, Heiko H. Ludwig
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Patent number: 12299410Abstract: A computer-implemented method for generating hash values to determine string similarity is disclosed. The computer-implemented method includes converting a first text string of a first data set into a first set of shingles. The computer-implemented method further includes determining a weight associated with each shingle in the first set of shingles based, at least in part, on a particular record field associated with a shingle. The computer-implemented method further includes generating, based on a hash function, a hash value for each shingle in the first set of shingles. The computer-implemented method further includes reducing the hash value generated for each shingle in the first set of shingles, based, at least in part on the weight associated with the shingle.Type: GrantFiled: June 30, 2022Date of Patent: May 13, 2025Assignee: International Business Machines CorporationInventors: Allon Adir, Ehud Aharoni, Omri Soceanu, Michael Mirkin
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Patent number: 12289393Abstract: Mechanisms are provided for performing a tournament selection process of a computer function. A request is received to execute the computer function on an input vector data structure, where a result of the computer function is provided by executing the tournament selection process. The input vector data structure is received, comprising a plurality of values where each value corresponds to a vector slot. An index vector data structure is received that comprises indices of the vector slots of the input vector. Iteration(s) of the tournament selection process are executed to identify a value in the input vector satisfying a criterion of the computer function. An operation is performed on the index vector data structure to generate an indicator vector data structure that uniquely identifies a slot in the input vector data structure that is a result of the computer function being executed on the input vector data structure.Type: GrantFiled: November 22, 2022Date of Patent: April 29, 2025Assignee: International Business Machines CorporationInventors: Ramy Masalha, Ehud Aharoni, Nir Drucker, Gilad Ezov, Hayim Shaul, Omri Soceanu
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Patent number: 12284265Abstract: A method and system for evaluating and selecting an optimal packing solution (or solutions) for data that is run through a fully homomorphic encryption (FHE) simulation. In some instances, a user selected model architecture is provided in order to start simulating multiple potential configurations. Additionally, the cost of each simulated configuration is taken into account when determining an optimal packing solution.Type: GrantFiled: June 27, 2022Date of Patent: April 22, 2025Assignee: International Business Machines CorporationInventors: Omri Soceanu, Gilad Ezov, Ehud Aharoni
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Patent number: 12255980Abstract: A computer-implemented method comprising: generating, from a key-seed associated with a user, a set of homomorphic encryption (HE) keys associated with an HE scheme; receiving, from a key management system (KMS) associated with said HE scheme, an encrypted version of said key-seed; storing said encrypted version of said key-seed, and said set of HE keys, in an untrusted storage location; and at a decryption stage, decrypting an encrypted computation result generated using said HE scheme, by: (i) recalling, from said untrusted storage location, said encrypted version of said key-seed, (ii) providing said encrypted version of said key-seed to said KMS, to obtain a decrypted version of said key-seed s associated with said user, (iii) generating, from said received decrypted version of said key-seed, a secret HE key associated with said HE scheme, and (iv) using said secret HE key to decrypt said encrypted computation result.Type: GrantFiled: January 3, 2023Date of Patent: March 18, 2025Assignee: International Business Machines CorporationInventors: Akram Bitar, Dov Murik, Ehud Aharoni, Nir Drucker, Omri Soceanu, Ronen Levy
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Patent number: 12192321Abstract: A second set of data identifiers, comprising identifiers of data usable in federated model training by a second data owner, is received at a first data owner from the second data owner. An intersection set of data identifiers is determined at the first data owner. At the first data owner according to the intersection set of data identifiers, the data usable in federated model training is rearranged by the first data owner to result in a first training dataset. At the first data owner using the intersection set of data identifiers, the first training dataset, and a previous iteration of an aggregated set of model weights, a first partial set of model weights is computed. An updated aggregated set of model weights, comprising the first partial set of model weights and a second partial set of model weights from the second data owner, is received from an aggregator.Type: GrantFiled: July 28, 2022Date of Patent: January 7, 2025Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Runhua Xu, Nathalie Baracaldo Angel, Hayim Shaul, Omri Soceanu
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Publication number: 20240413966Abstract: A technique for privacy-preserving homomorphic inferencing using a neural network having an activation function, such as a non-linear high-degree polynomial. The network is trained to learn input features of an input feature vector together with their associated inverses. During inferencing, an encrypted data point is received at the network. The data point comprises an input feature vector that has been extended with a set of one or more additional feature values, the set of one or more additional feature values having been generated by applying a normalized inverse function to respective one or more features in the feature vector. Homomorphic inferencing is performed on the encrypted data point using the machine learning model to generate an encrypted result, which is then returned. By applying the normalized inverse function, the high-degree polynomial can use any value of an input feature during inferencing, whether the value is within or outside of a particular input range.Type: ApplicationFiled: June 8, 2023Publication date: December 12, 2024Applicant: International Business Machines CorporationInventors: Omri Soceanu, Allon Adir, Omer Yehuda Boehm, Boris Rozenberg, Eyal Kushnir, Ehud Aharoni
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Patent number: 12149607Abstract: Mechanisms are provided for fully homomorphic encryption enabled graph embedding. An encrypted graph data structure, having encrypted entities and predicates, is received and, for each encrypted entity, a corresponding set of entity ciphertexts is generated based on an initial embedding of entity features. For each encrypted predicate, a corresponding predicate ciphertext is generated based on an initial embedding of predicate features. A machine learning process is iteratively executed, on the sets of entity ciphertexts and the predicate ciphertexts, to update embeddings of the entity features of the encrypted entities and update embeddings of predicate features of the encrypted predicates, to generate a computer model for embedding entities and predicates. A final embedding is output based on the updated embeddings of the entity features and predicate features of the computer model.Type: GrantFiled: October 10, 2022Date of Patent: November 19, 2024Assignee: International Business Machines CorporationInventors: Allon Adir, Ramy Masalha, Eyal Kushnir, Omri Soceanu, Ehud Aharoni, Nir Drucker, Guy Moshkowich
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Publication number: 20240370767Abstract: An example system includes a processor to train and stabilize a machine learning model using public data. The processor can fine-tune the machine learning model using anonymized private data. The processor can fine-tune the machine learning model using encrypted private data.Type: ApplicationFiled: May 3, 2023Publication date: November 7, 2024Inventors: Moran BARUCH, Nir DRUCKER, Omri SOCEANU
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ACCELERATING PRIVACY-PRESERVING NEURAL NETWORKS AND AN EFFICIENT SKIP-CONNECTION REALIZATION THEREOF
Publication number: 20240330686Abstract: A skip-connections analysis method, system, and computer program product for accelerating neural networks by removing skip-connections and efficient skip-connection realization.Type: ApplicationFiled: March 29, 2023Publication date: October 3, 2024Inventors: Itamar Zimerman, Nir Drucker, Moran Baruch, Omri Soceanu -
TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS
Publication number: 20240291655Abstract: An example system can include a processor to receive a ciphertext including a fully homomorphic encrypted (FHE) time series from a client device. The processor can train an ARIMA model on the ciphertext using an estimated error and approximating polynomials. The processor can generate an encrypted report and send the encrypted report to the client device.Type: ApplicationFiled: February 23, 2023Publication date: August 29, 2024Inventors: Allon ADIR, Ramy MASALHA, Eyal KUSHNIR, Ehud AHARONI, Omri SOCEANU -
Publication number: 20240256850Abstract: A trained neural network is partitioned into a client-side portion and a server-side portion, the client-side portion comprising a first set of layers of the trained neural network, the server-side portion comprising a second set of layers of the trained neural network, the trained neural network trained using a first set of training data. From a homomorphically encrypted intermediate result input to the server-side portion, a homomorphically encrypted output of the trained neural network is computed, the homomorphically encrypted intermediate result comprising a homomorphically encrypted output computed by the client-side portion.Type: ApplicationFiled: January 30, 2023Publication date: August 1, 2024Applicant: International Business Machines CorporationInventors: Omri Soceanu, Nir Drucker, Subhankar Pal, Roman Vaculin, Kanthi Sarpatwar, Alper Buyuktosunoglu, Pradip Bose, Hayim Shaul, Ehud Aharoni, James Thomas Rayfield
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Publication number: 20240243898Abstract: A technique to remotely identify potential compromise of a service provider that performs homomorphic inferencing on a model. For a set of real data samples on which the inferencing is to take place, at least first and second permutations of a set of trigger samples are generated. Every set of samples (both trigger and real samples) are then sent for homomorphic inferencing on the model at least twice, and in a secret permutated way. To improve performance, a permutation is packaged with the real data samples prior to encryption using a general purpose data structure, a tile tensor, that allows users to store multi-dimensional arrays (tensors) of arbitrary shapes and sizes. In response to receiving one or more results from the HE-based model inferencing, a determination is made whether the service provider is compromised. Upon a determination that the service provider is compromised, a given mitigation action is taken.Type: ApplicationFiled: January 17, 2023Publication date: July 18, 2024Applicant: International Business Machines CorporationInventors: Eyal Kushnir, Ramy Masalha, Omri Soceanu, Nir Drucker
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Patent number: 12041157Abstract: Privacy-preserving homomorphic inferencing utilizes batch processing on encrypted data records. Each data record has a private data portion of interest against which the inferencing is carried out. Batch processing is enabled with respect to a set of encrypted data records by techniques that ensure that each encrypted data record has its associated private data portion in a unique location relative to the other data records. The set of encrypted data records are then summed to generate a single encrypted data record against which the inferencing is done. In a first embodiment, the private data portions of interest are selectively and uniquely positioned at runtime (when the inferencing is being applied). In a second embodiment, the private data portions of interest are initially positioned with the data-at-rest, preferably in an off-line process; thereafter, at runtime individual encrypted data records are processed as necessary to adjust the private data portions to unique positions prior to batching.Type: GrantFiled: May 10, 2021Date of Patent: July 16, 2024Assignee: International Business Machines CorporationInventors: Kanthi Sarpatwar, Roman Vaculin, Ehud Aharoni, James Thomas Rayfield, Omri Soceanu
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Publication number: 20240223355Abstract: A computer-implemented method comprising: generating, from a key-seed associated with a user, a set of homomorphic encryption (HE) keys associated with an HE scheme; receiving, from a key management system (KMS) associated with said HE scheme, an encrypted version of said key-seed; storing said encrypted version of said key-seed, and said set of HE keys, in an untrusted storage location; and at a decryption stage, decrypting an encrypted computation result generated using said HE scheme, by: (i) recalling, from said untrusted storage location, said encrypted version of said key-seed, (ii) providing said encrypted version of said key-seed to said KMS, to obtain a decrypted version of said key-seed s associated with said user, (iii) generating, from said received decrypted version of said key-seed, a secret HE key associated with said HE scheme, and (iv) using said secret HE key to decrypt said encrypted computation result.Type: ApplicationFiled: January 3, 2023Publication date: July 4, 2024Inventors: Akram Bitar, Dov Murik, Ehud Aharoni, Nir Drucker, OMRI SOCEANU, Ronen Levy
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Publication number: 20240171375Abstract: Mechanisms are provided for performing a tournament selection process of a computer function. A request is received to execute the computer function on an input vector data structure, where a result of the computer function is provided by executing the tournament selection process. The input vector data structure is received, comprising a plurality of values where each value corresponds to a vector slot. An index vector data structure is received that comprises indices of the vector slots of the input vector. Iteration(s) of the tournament selection process are executed to identify a value in the input vector satisfying a criterion of the computer function. An operation is performed on the index vector data structure to generate an indicator vector data structure that uniquely identifies a slot in the input vector data structure that is a result of the computer function being executed on the input vector data structure.Type: ApplicationFiled: November 22, 2022Publication date: May 23, 2024Inventors: Ramy Masalha, Ehud Aharoni, Nir Drucker, Gilad Ezov, Hayim Shaul, Omri Soceanu
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Publication number: 20240126557Abstract: An example system includes a processor that can receive a number of complex packed tensors, wherein each of the complex packed tensors include real numbers encoded as imaginary parts of complex numbers. The processor can execute a single instruction, multiple data (SIMD) operation on the complex packed tensors using an integrated circuit of real and complex packed tensors in a complex domain to generate a result.Type: ApplicationFiled: September 30, 2022Publication date: April 18, 2024Inventors: Hayim SHAUL, Nir DRUCKER, Ehud AHARONI, Omri SOCEANU, Gilad EZOV
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Publication number: 20240121074Abstract: Mechanisms are provided for fully homomorphic encryption enabled graph embedding. An encrypted graph data structure, having encrypted entities and predicates, is received and, for each encrypted entity, a corresponding set of entity ciphertexts is generated based on an initial embedding of entity features. For each encrypted predicate, a corresponding predicate ciphertext is generated based on an initial embedding of predicate features. A machine learning process is iteratively executed, on the sets of entity ciphertexts and the predicate ciphertexts, to update embeddings of the entity features of the encrypted entities and update embeddings of predicate features of the encrypted predicates, to generate a computer model for embedding entities and predicates. A final embedding is output based on the updated embeddings of the entity features and predicate features of the computer model.Type: ApplicationFiled: October 10, 2022Publication date: April 11, 2024Inventors: Allon Adir, Ramy Masalha, Eyal Kushnir, OMRI SOCEANU, Ehud Aharoni, Nir Drucker, GUY MOSHKOWICH
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Patent number: 11947444Abstract: Embodiments may provide techniques that may provide more accurate and actionable alerts by cloud workload security systems so as to improve overall cloud workload security. For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise generating performance and security information relating to a software system during development of the software system, generating performance and security information relating to the software system during deployed operation of the software system, matching the performance and security information generated during development of the software system with the performance and security information generated during deployed operation of the software system to determine performance and security alerts to escalate, and reporting the escalated performance and security alerts.Type: GrantFiled: November 6, 2020Date of Patent: April 2, 2024Assignee: International Business Machines CorporationInventors: Fady Copty, Omri Soceanu, Gilad Ezov, Ronen Levy
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Publication number: 20240089081Abstract: An example system includes a processor to compute a tensor of indicators indicating a presence of partial sums in an encrypted vector of indicators. The processor can also securely reorder an encrypted array based on the computed tensor of indicators to generate a reordered encrypted array.Type: ApplicationFiled: August 25, 2022Publication date: March 14, 2024Inventors: Eyal KUSHNIR, Hayim SHAUL, Omri SOCEANU, Ehud AHARONI, Nathalie BARACALDO ANGEL, Runhua XU, Heiko H. LUDWIG