A method for creating a hierarchical threshold signature digital asset wallet using a hierarchical distributed key generator (DKG) and a signature protocol includes steps of generating a public key by users and the digital asset wallet service platform, securing and controlling a portion of shares, sending a transaction signing request, validating the transaction signing request, creating a signature of the signed transaction, and uploading the signed transaction to the corresponding digital asset blockchain network and monitoring the execution of the signed transaction.

The present disclosure relates to systems, methods, and computer-readable media for enhancing security of communications between instances of clients and servers while enabling rotation of server certificates (e.g., X.509 certificates). The systems described herein involve updating a client list of server certificates (e.g., a certificate thumbprint) without reconfiguring or re-installing a client and/or server application, starting a new session (e.g., a hypertext transfer protocol secure (HTTPS) session), or deploying new code. The systems described herein may passively or actively update a client list of certificates to enable a client to security verify an identity of a server instance in a non-invasive way that boosts security from man-in-the-middle types of attacks.

Disclosed is a method for each party of a group of m parties to be able to learn an Nth smallest value in a combined list. The method includes providing a value R.sub.i to a group of members; computing how many numbers are smaller than R.sub.i in a respective list of values for each respective member of the group of members; computing, a total number of smaller values (P.sub.i); identifying a position of R.sub.i in a combined list of values comprising each respective list of values; when N=P.sub.i+1, returning R.sub.i; when N is greater than P.sub.i+1, removing all values smaller than R.sub.i in their respective list of values and setting N=N−(P.sub.i+1); when N is less than P.sub.i+1, removing all numbers bigger than R.sub.i in their respective list of value; and setting i=i+1.

New and efficient protocols are provided for privacy-preserving machine learning training (e.g., for linear regression, logistic regression and neural network using the stochastic gradient descent method). A protocols can use the two-server model, where data owners distribute their private data among two non-colluding servers, which train various models on the joint data using secure two-party computation (2PC). New techniques support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions, such as sigmoid and softmax.

Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

A method for checking the identity of a reference individual, the method comprising the following steps, implemented by a checking device: selecting terminals respectively associated with individuals forming part of a set of individuals whose identities are intended to be checked by the checking device, the individual forming part of the set of individuals; sending, to each of the selected terminals, an input datum associated with the reference individual and a request asking the terminal to implement a first cryptographic processing operation producing an output datum from the input datum and from a private key specific to the individual associated with the terminal; receiving each output datum; and implementing a second cryptographic processing operation producing a check result relating to the reference individual from each output datum.

An enhanced robust input protocol for secure multi-party computation (MPC) via pseudorandom secret sharing is provided. With this enhanced protocol, the servers that participate in MPC can generate and send a single random sharing [R] to a client with k inputs (rather than a separate random sharing per input), and the client can derive k pseudorandom sharings from [R] without any further server interactions.

A calculation of a gradient descent method in secure computing is performed at high speed while maintaining accuracy. A secure gradient descent computation method calculates a gradient descent method while keeping a gradient and a parameter concealed. An initialization unit initializes concealed values [M], [V] of matrices M, V (S11). A gradient calculation unit determines concealed value [G] of a matrix G of a gradient g (S12). A parameter update unit calculates [M] β1 [M]+(1−β1) [G] (S13-1), calculates [V]←β2 [V]+(1−β2) [G]◯[G] (S13-2), calculates [M{circumflex over ( )}]←β{circumflex over ( )}1, t [M] (S13-3), calculates [V{circumflex over ( )}]←β{circumflex over ( )}2, t [V] (S13-4), calculates [G{circumflex over ( )}]←Adam ([V{circumflex over ( )}]) (S13-5), calculates [G{circumflex over ( )}]←[G{circumflex over ( )}]◯[M{circumflex over ( )}] (S13-6), and calculates [W]←[W]−[G{circumflex over ( )}] (S13-7).

A search key is generated (S20). A key relationship array is transmitted (S11). If an element matching the key relationship array is present, the found search key is held (S21). A key relationship index is transmitted (S22). A record read out using the key relationship index is transmitted (S12). If the record matches the search key, the found search key is held (S23). The found search key is set for an empty element of the key relationship array and is transmitted (S24). A data array is transmitted (S13). If an element matching the data array is present, the found data is held (S25). A data index is transmitted (S26). A record read out using the data index is transmitted (S14). If the record matches the search key, the found data is held (S27). Desired data is set for an empty element of the data array and is transmitted (S28).

An apparatus is one of a plurality of apparatuses that participate in multi-party computation and the apparatus implements a protocol to perform zero-knowledge proof in secret-distribution-based multi-party computation. The apparatus includes an acquisition unit that acquires a share of data related to a matter to be certified, and an output unit that outputs an output share obtained as a result of performing calculation according to the protocol using the acquired share as an input. Verification in zero-knowledge proof can be performed using output shares collected from the plurality of apparatuses participating in the multi-party computation.