Abstract: Disclosed herein are systems and methods for dynamic job performance in secure multiparty computation (SMPC). The method may comprise receiving an SMPC query that indicates a processing job to be performed on a data input. The method may split the data input to generate a plurality of partial data inputs, based on parameters and the query type of the SMPC query. The method may generate a plurality of jobs to perform on the plurality of partial data inputs and determine a combined result of the processing job. The method may adjust the amount of worker processes in a worker pool based on at least one of: required computation, time of day, date, financial costs, power consumption, and available network bandwidth.

Abstract: Disclosed herein are systems and methods for preserving data integrity when integrating secure multiparty computation (SMPC) and blockchain technology. In one exemplary aspect, a method may split, via a data publisher, data into a plurality of data secret shares using an SMPC protocol, wherein each secret share of the plurality of data secret shares is assigned to an SMPC compute node of a plurality of SMPC compute nodes and wherein the plurality of SMPC compute nodes may be members of a blockchain network. In some aspects, the method may determine parameters of a message authentication code (MAC) condition based on the data, may generate secret shares of the MAC condition parameters, and may include a plurality of MAC secret shares with the plurality of data secret shares.

Abstract: Disclosed herein are systems and methods for synchronizing anonymized linked data across multiple queues for SMPC. The systems and methods guarantee that data is kept private from a plurality of nodes, yet can still be synced within a local queue, across the plurality of local queues. In conventional SMPC frameworks, specialised data known as offline data is required to perform key operations, such as multiplication or comparisons. The generation of this offline data is computationally intensive, and thus adds significant overhead to any secure function. The disclosed system and methods aid in the operation of generating and storing offline data before it is required. Furthermore, the disclosed system and methods can help start functions across multi-parties, preventing concurrency issues, and align secure input data to prevent corruption.

Abstract: Disclosed herein are systems and methods for preserving data integrity when integrating secure multiparty computation (SMPC) and blockchain technology. In one exemplary aspect, a method may split, via a data publisher, data into a plurality of data secret shares using an SMPC protocol, wherein each secret share of the plurality of data secret shares is assigned to an SMPC compute node of a plurality of SMPC compute nodes and wherein the plurality of SMPC compute nodes may be members of a blockchain network. In some aspects, the method may determine parameters of a message authentication code (MAC) condition based on the data, may generate secret shares of the MAC condition parameters, and may include a plurality of MAC secret shares with the plurality of data secret shares.

Abstract: Disclosed herein are systems and methods for generating tokens using SMPC compute engines. In one aspect, a method may hash, by a node, a data input with a salt value. The method may split, by the node, the hashed data input into a plurality of secret shares, wherein each respective secret share of the plurality of secret shares is assigned to a respective SMPC compute engine of a plurality of SMPC compute engines. The respective SMPC compute engines may be configured to collectively hash the respective secret share with a secret salt value, unknown to the plurality of SMPC compute engines. The respective SMPC compute engine may further receive a plurality of hashed secret shares from remaining SMPC compute engines of the plurality of SMPC compute engines, and generate a token, wherein the token is a combination of the hashed respective secret share and the plurality of hashed secret shares.

Abstract: Disclosed herein are systems and methods for dynamic job performance in secure multiparty computation (SMPC). The method may comprise receiving an SMPC query that indicates a processing job to be performed on a data input. The method may split the data input to generate a plurality of partial data inputs, based on parameters and the query type of the SMPC query. The method may generate a plurality of jobs to perform on the plurality of partial data inputs and determine a combined result of the processing job. The method may adjust the amount of worker processes in a worker pool based on at least one of: required computation, time of day, date, financial costs, power consumption, and available network bandwidth.

Abstract: Disclosed herein are systems and methods for generating tokens using SMPC compute engines. In one aspect, a method may hash, by a node, a data input with a salt value. The method may split, by the node, the hashed data input into a plurality of secret shares, wherein each respective secret share of the plurality of secret shares is assigned to a respective SMPC compute engine of a plurality of SMPC compute engines. The respective SMPC compute engines may be configured to collectively hash the respective secret share with a secret salt value, unknown to the plurality of SMPC compute engines. The respective SMPC compute engine may further receive a plurality of hashed secret shares from remaining SMPC compute engines of the plurality of SMPC compute engines, and generate a token, wherein the token is a combination of the hashed respective secret share and the plurality of hashed secret shares.

Abstract: Disclosed herein are systems and methods for synchronizing anonymized linked data across multiple queues for SMPC. The systems and methods guarantee that data is kept private from a plurality of nodes, yet can still be synced within a local queue, across the plurality of local queues. In conventional SMPC frameworks, specialised data known as offline data is required to perform key operations, such as multiplication or comparisons. The generation of this offline data is computationally intensive, and thus adds significant overhead to any secure function. The disclosed system and methods aid in the operation of generating and storing offline data before it is required. Furthermore, the disclosed system and methods can help start functions across multi-parties, preventing concurrency issues, and align secure input data to prevent corruption.