Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

The present invention relates generally to a system and method of querying an anonymized database. More particularly, the invention provides a method and system for querying an anonymized database without the need to decrypt queried data while it's processed. Even more specifically, the invention provides a method and system of anonymizing a database such that it may be queried efficiently in near real time while still retaining the ability to not decrypt requested data while it's being processed.

A device, system and method for securely executing recursive computations over encrypted data in a homomorphically encrypted (HE) space. For a recursive algorithm with sequentially dependent recursive iterations, executing the recursive algorithm in parallel by computing multiple recursive iterations simultaneously over multiple parallel execution iterations and not in sequential order. Each parallel execution iteration may compute a partial HE solution of multiple sequential recursive iterations comprising a known HE part and leaves empty a placeholder call slot for an unknown HE part. Placeholder call slots remain empty and are filled at delayed times at a later parallel execution iteration from when the known part of the same HE computation is computed. A final HE solution is computed in fewer multiple parallel execution iterations than the number of sequential recursive iterations, thereby accelerating the recursive algorithm in HE space.

A method for verifying a material data chain (MDC) that is maintained by a creator is disclosed. The method includes receiving an unverified portion of the MDC from the creator including a set of consecutive material data blocks (MDBs). Each respective MDB includes respective material data, respective metadata, and a creator verification value. The method includes modifying a genomic differentiation object assigned to the verification cohort based on first genomic regulation instructions (GRI) that were used by the creator to generate the creator verification value. For each MDB in the unverified portion, the method includes determining a verifier verification value based on the MDB, a preceding MDB in the MDC, and a genomic engagement factor (GEF) determined with respect to the MDB. The GEF corresponding to an MDB is determined by extracting a sequence from the metadata of a MDB and mapping the sequence into the modified genomic differentiation object.

Fully homomorphic encryption integrated circuit (IC) chips, systems and associated methods are disclosed. In one embodiment, a method of operation for a number theoretic transform (NTT) butterfly circuit is disclosed. The (NTT) butterfly circuit includes a high input word path cross-coupled with a low word path. The high input word path includes a first adder/subtractor, and a first multiplier. The low input word path includes a second adder/subtractor, and a second multiplier. The method includes selectively bypassing the second adder/subtractor and the second multiplier, and reconfiguring the low and high input word paths into different logic processing units in response to different mode control signals.

Systems and methods for preserving privacy in dark pool trading environments are provided. The methods include receiving buy orders that include encrypted buy order information; receiving sell orders that include encrypted sell order information; determining whether at least one received buy order matches with at least one received sell order; and when there is a match, executing a transaction based on the match. The determination is made without revealing the encrypted information to an operator of the dark pool, thereby preserving the confidentiality of the information until the transaction is executed.

An example operation may include one or more of receiving, by a retailer node, an encrypted inventory of goods data from a plurality of supplier nodes over a blockchain network, computing, by the retailer node, an ordering proportion based on the encrypted inventory of goods data, generating, by the retailer node, an ordering policy based on the ordering proportion, and executing a smart contract to order goods from the plurality of the supplier nodes based on the ordering policy.

A secure multi-party computation method and apparatus, a device, and a storage medium are provided, which belong to the field of data security technologies. The method includes: performing homomorphic encryption processing on first plaintext data to generate first ciphertext data; transmitting encrypted data containing the first ciphertext data to a second node device; receiving difference ciphertext data transmitted by the second node device; and decrypting the difference ciphertext data to obtain a positive and negative property of a difference between the first plaintext data and the second plaintext data. The foregoing method and apparatus, device, and storage medium are conducive to reducing the number of times of communications of secure multi-party computation, have low communication overhead and high computation efficiency, and enable magnitude comparison and equality testing to be performed simultaneously.

A system and method for executing a record within an immutable sequential data structure, the system including a computing device, the computing device configured to transmit a communication to a remote device, receive a remark from the remote device, retrieve an input related to a user, wherein the input is stored as an encrypted proof-linked assertion on at least an immutable sequential data structure for authorized party access, generate a record as a function of the input, transmit the record to the remote device, and store an executed record within the at least an immutable sequential data structure.

A heterogeneous processing system for federated learning and privacy-preserving computation, including: a serial subsystem configured for distributing processing tasks and configuration information of processing tasks, the processing task indicating performing an operation corresponding to computing mode on one or more operands; and a parallel subsystem configured for, based on the configuration information, selectively obtaining at least one operand of the one or more operands from an intermediate result section on the parallel subsystem while obtaining remaining operand(s) of the one or more operands with respect to the at least one operand from the serial subsystem, and performing the operation on the operands obtained based on the configuration information.