A computing system retrieves a credit check algorithm. The credit check algorithm utilizes one or more parameters for evaluation of a credit score of an individual. The computing system identifies a plurality of entities contributing parameters for the evaluation of the credit score of the individual. The computing system compiles the credit check algorithm into a plurality of components. Each component corresponds to a respective entity of the plurality of entities and each component generates an output unique to the respective entity. The computing system transmits each component to a respective entity of the plurality of entities. The computing system instructs each entity to share a respective output with each remaining entity. The computing system receives a credit score for the individual from each of the plurality of entities. Each credit score received from each entity is the same.

Embodiments of the invention include systems and methods for protecting study participant data for aggregate analysis. Aspects include sending a broker encryption key to a plurality of subjects. Aspects also include receiving double-encrypted subject data from the plurality of subjects. Aspects also include decrypting the double-encrypted subject data with a broker decryption key to generate single-encrypted subject data for the plurality of subjects. Aspects also include aggregating the single-encrypted subject data for the plurality of subjects to generate an aggregated single-homomorphically encrypted data set. Aspects also include including a plurality of random factors in the aggregated single-encrypted data set. Aspects also include sending the aggregated single-homomorphically encrypted data set to a researcher.

Provided are methods and systems for performing a secure machine learning analysis over an instance of data. An example method includes acquiring, by a client, an homomorphic encryption scheme, and at least one machine learning model data structure. The method further includes generating, using the encryption scheme, at least one homomorphically encrypted data structure, and sending the encrypted data structure to at least one server. The method includes executing a machine learning model, by the at least one server based on the encrypted data structure to obtain an encrypted result. The method further includes sending, by the server, the encrypted result to the client where the encrypted result is decrypted. The machine learning model includes neural networks and decision trees.

Various embodiments include a coordinator node for coordinating a multiparty computation (MPC) on one or more datasets. The system comprises a plurality of client nodes, one or more datasets and a plurality of computation nodes. Client nodes may include at least one dataset and/or at least one computation node that can operate as a party to an MPC. The coordinator node is configured to receive a request for an MPC on one or more of the datasets from a requesting node, the MPC including the evaluation of at least one function by two or more computation nodes from different client nodes; determine a computation schedule for the MPC, the computation schedule indicating which client nodes of the plurality of client nodes are to participate in the MPC; send at least part of the determined computation schedule to at least one of the client nodes indicated in the determined computation schedule.

A transaction consensus processing method for a blockchain is provided. A target node that initiates a proposition performs compression processing on proposed transaction data based on a compression algorithm, and fragments the compressed transaction data into a number of data fragments based on an erasure code algorithm. The method includes: receiving a data fragment of the transaction data that is sent by the target node in a unicast mode, data fragments sent by the target node to nodes in the unicast mode being different; broadcasting the received data fragment to other nodes, and receiving data fragments of the transaction data that are broadcast by the other nodes; performing data recovery on the received data fragment based on an erasure code reconstruction algorithm, performing decompression processing on the recovered transaction data based on a decompression algorithm to obtain original content of the transaction data, and completing the consensus.

An aggregate median is efficiently obtained while confidentiality is kept. An order computing part generates ascending order a and descending order d within a group when a table which has been stably sorted based on a desired value attribute and a key attribute is grouped based on the key attribute. A subtracting part generates shares {a−d}, {d−a} of a−d, d−a. A bit deleting part generates shares {a′}, {d′} of a′, d′ obtained by excluding least significant bits from {a−d}, {d−a}. An equality determining part generates shares {a″}, {d″} of {a″}:={|a′=0|}, {d″}:={|d′=0|}. A format converting part (15) converts {a″}, {d″} into [a″], [d″]. A flag applying part generates shares [v.sub.a], [v.sub.d] of [v.sub.a]:=[v.sub.1a″], [v.sub.d]:=[v.sub.1d″]. A permutation generating part generates shares {{σ.sub.a}}, {{σ.sub.d}} of permutations σ.sub.a, σ.sub.d which sort ¬a″, ¬d″. A median computing part generates a share [x] of a vector x.

A system for protecting personal information uses a challenge and an encrypted copy of the challenge in the form of a message authentication code (MAC) to provide authentication among multiple parties. The challenge is received by a first party from a second party. The challenge is encrypted by the first party to form the MAC and then both the challenge and the MAC are returned to the second party. The second party authenticates the first party by confirming the challenge. The second party sends the MAC and challenge to the third party. The third party decrypts the MAC using a key shared with the first party. When the decrypted MAC matches the challenge, the first party is authenticated to the third party. The process is applicable to transaction processing to limit compromise of payment instrument details.

An artificial intelligence calculation semiconductor device is provided. The artificial intelligence calculation semiconductor device comprising: a control unit; and a MAC (Multiply and Accumulator) calculator which executes a homomorphic encryption calculation through the control unit, wherein the MAC calculator includes an NTT (Numeric Theoretic Transform)/INTT (Inverse NTT) circuit which generates cipher texts by performing a homomorphic multiplication calculation through transformation or inverse transformation of data, a cipher text multiplier which executes a multiplication calculation between the cipher texts, a cipher text adder/subtractor which executes addition and/or subtraction calculations between the cipher texts, and a rotator which performs a cyclic shift of a slot of the cipher texts.

A private key management system (PKMS) that may include a first agent configured to receive a request from a client device; a distributed ledger shared between the first agent and multiple second agents such that the distributed ledger operates based on a consensus algorithm; a validation engine maintained by each of the first agent and the multiple second agents, the validation engine configured to query the distributed ledger to obtain data to verify the request; and a vault module maintained by each of the first agent and the multiple second agents, the vault module configured to perform a cryptography operation based on the request after the validation engine verifies the request.

This invention pertains to secure communications between multiple parties and/or secure computation or data transmission between multiple computers or multiple vehicles. This invention provides a secure method for three or more parties to establish one or more shared secrets between all parties. In some embodiments, there are less than 40 parties and in other embodiments there are more than 1 million parties that establish a shared secret. In some embodiments, establishing a shared secret among multiple parties provides a method for a secure conference call. In some embodiments, a shared secret is established with multiple computer nodes across the whole earth to help provide a secure Internet infrastructure that can reliably and securely route Internet traffic. In some embodiments, a shared secret is established so that self-driving vehicles may securely communicate and securely coordinate their motion to avoid collisions. In some embodiments, a shared secret is established with multiple computer nodes that participate as a network, performing blockchain computations.