A first terminal holds first encrypted data encrypted by using a first key by a first encryption scheme having deterministic and commutativity, a second terminal holds second encrypted data encrypted by using a second key by the first encryption scheme, the first terminal transmits the first encrypted data to the second terminal, the second terminal transmits the second encrypted data to the first terminal, the first terminal generates third encrypted data by encrypting the second encrypted data by using the first key by the first encryption scheme, the third encrypted data is transmitted to the second terminal, the second terminal decrypts the third encrypted data with the second key, and calculates a common part between the second encrypted data and the decrypted third encrypted data, and transmits the common part to the first terminal, and the first terminal decrypts the common part with the first key.

Techniques for implementing distributed registration and authentication via threshold secret sharing and additively homomorphic encryption are provided. A threshold secret sharing scheme is a cryptographic method for sharing a secret among N parties in a manner that requires at least T+1 of the N parties to cooperate in order to reconstruct/reveal the secret, where T is some threshold value less than N. Additively homomorphic encryption is an encryption scheme that enables users to perform additive computations on encrypted data without first decrypting that data. With these techniques, a group of N nodes can efficiently perform distributed registration and authentication in a correct, secure, and privacy-preserving fashion, even if up to T of the N nodes are corrupted by an adversary.

A secure computation system comprises at least five secure computation server apparatuses connected to each other via a network and performs secure computation on a value stored while being secret-shared, and each of the secure computation server apparatuses has a comparative verification part that compares values, which should be the same, received from at least three secure computation server apparatuses and that accepts a received value identical to at least another received value as a correct value.

A secure multi-party computation implements real number arithmetic using modular integer representation on the backend. As part of the implementation, a secret shared value jointly stored by multiple parties in a first modular representation is cast into a second modular representation having a larger most significant bit. The parties use a secret shared masking value in the first representation, the range of which is divided into two halves, to mask and reveal a sum of the secret shared value and the secret shared masking value. The parties use a secret shared bit that identifies the half of the range that contains the masking value, along with the sum to collaboratively construct a set of secret shares representing the secret shared value in the second modular format. In contrast with previous work, the disclosed solution eliminates a non-zero probability of error without sacrificing efficiency or security.

A secure computing hardware apparatus includes at least a secret generator module, the at least a secret generator module configured to generate a module-specific secret, and a device identifier circuit communicatively connected to the at least a secret generator, the device identifier circuit configured to produce at least an output comprising a secure proof of the module-specific secret. Secret generator module may implement one or more physically unclonable functions to generate the module-specific secret.

In one embodiment, a method includes receiving a request for a transaction from a first party and identifying a second party for the transaction. The first party is associated with a first party decentralized identifier (DID), and the second party is associated with a second party DID. The method also includes receiving negotiation data from the first party and the second party and generating a data model using the first party DID, the second party DID, and the negotiation data. The method further includes generating a hybrid legal document using the data model and a legal prose document.

A remote security controller (RSC) generates a private key for a client application on a different host computing device and splits the private key into a first fragment and a second fragment. The first fragment, but not the second fragment, is encrypted using a symmetric key. The split private key is returned to the different host computing device. A local security controller (LSC) on the different host computing device is able to derive the symmetric key using a key agreement protocol with the RSC. When the client application needs to digitally sign a data value with the split private key, the client application generates a first partial Multiparty Computation (MPC) signature using the second fragment. The LSC generates a second partial MPC signature with the first fragment, which has been decrypted using the symmetric key. The first and second partial MPC signatures are combinable to digitally sign the data value.

A client application and a local security controller (LSC) executing on a host computing device use a Multiparty Computation (MPC) cryptographic key generation technique to create two fragments of a split private key, which are held by the client application and LSC, respectively. The client application generates a certificate signing request (CSR). The client application and LSC sign the CSR with the split private key using an MPC technique. The LSC then signs a token from the client application to indicate that the private key corresponding to the CSR is MPC-backed. A package with the CSR and the first and second signatures is then sent to a remote device acting as a certificate authority. The remote device verifies the two signatures and issues a certificate to the client application. The second signature is verified using information sent to the remote device from the LSC during a registration process.

A system and method are disclosed for providing a private multi-modal artificial intelligence platform. The method includes splitting a neural network into a first client-side network, a second client-side network and a server-side network and sending the first client-side network to a first client. The first client-side network processes first data from the first client, the first data having a first type. The method includes sending the second client-side network to a second client. The second client-side network processes second data from the second client, the second data having a second type. The first type and the second type have a common association. Forward and back propagation occurs between the client side networks and disparate data types on the different client side networks and the server-side network to train the neural network.

Disclosed are techniques for determining data relationships between privacy-restricted datapoints, sourced over a computer network, which require data privacy measures concealing at least some datapoints from other clients in the network that the datapoint respectively do not originate from. A first client encrypts a first datapoint with a public key of a public/private encryption scheme and communicates it to the second client along with the public key. The second client encrypts a corresponding second datapoint with the public key, then determines a relationship between the two encrypted datapoints, and communicates the determined relationship to a central client along with the public key. Random noise is encrypted by the central client and added to the determined relationship, then sent together to the first client, followed by decryption by the first client using the private key. The central client extracts the random noise after receiving the decrypted determined relationship.