A first biometric sample of a user is received by an access device from a user device. First biometric information is generated in an obscured format, based on the first biometric sample. A plurality of biometric information is received in an obscured format. The plurality of biometric information corresponds to a plurality of users, and was obtained from biometric samples of the plurality of users. The first biometric information in the obscured format is compared to the plurality of biometric information in the obscured format, and a match result is generated based on the comparing. The match result is provided to a server computer. Based on the match result, information indicating that one of the plurality of users that is associated with one of the plurality of biometric information is the user associated with the first biometric information is received.

A method of performing ordered statistics between at least two parties is disclosed which includes identifying a first dataset (x.sub.A) by a first node (A), identifying a second dataset (x.sub.B) by a second node (B), wherein x.sub.B is unknown to A and x.sub.A is unknown to B, and wherein A is in communication with B, and wherein A and B are in communication with a server (S), A and B each additively splitting each member of their respective datasets into corresponding shares, sharing the corresponding shares with one another, arranging the corresponding shares according to a mutually agreed predetermined order into corresponding ordered shares, shuffling the ordered shares into shuffled shares, re-splitting the shuffled shares into re-split shuffled shares, and performing an ordered statistical operation on the re-split shuffled shares, wherein the steps of shuffle and re-split is based on additions, subtractions but not multiplication and division.

Systems and methods of cryptographic key distribution in a plurality of networks, including: sharing, by a first device, a first portion of a first cryptographic key controlled by a server with a second device, sharing, by the second device, a first portion of a second cryptographic key with the first device, signing a first transaction on a first network with data exchange from a first threshold signature address controlled by the first device, to a third address when one or more details of the first transaction are validated by the server; and signing a second transaction on a second network with data exchange from the second threshold signature address controlled by the second device to a fourth address when one or more details of the second transaction are validated by the server.

Disclosed is a process for testing a suspect model to determine whether it was derived from a source model. An example method includes receiving, from a model owner node, a source model and a fingerprint associated with the source model, receiving a suspect model at a service node, based on a request to test the suspect model, applying the fingerprint to the suspect model to generate an output and, when the output has an accuracy that is equal to or greater than a threshold, determining that the suspect model is derived from the source model. Imperceptible noise can be used to generate the fingerprint which can cause predictable outputs from the source model and a potential derivative thereof.

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.

Disclosed is a method that includes training, at a client, a part of a deep learning network up to a split layer of the client. Based on an output of the split layer, the method includes completing, at a server, training of the deep learning network by forward propagating the output received at a split layer of the server to a last layer of the server. The server calculates a weighted loss function for the client at the last layer and stores the calculated loss function. After each respective client of a plurality of clients has a respective loss function stored, the server averages the plurality of respective weighted client loss functions and back propagates gradients based on the average loss value from the last layer of the server to the split layer of the server and transmits just the server split layer gradients to the respective clients.

The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Developing a cyber security protocol to enable two members of a community to conduct a conversation without revealing neither their identity, nor the fact that a conversation took place. Secret randomized matching is used to allow people to claim certain personal attributes like age, place of residence, having a license, but without exposing their individual identity.

A system and method are disclosed for providing an averaging of models for federated learning and blind learning systems. The method includes selecting, at a server, a generator g and a number p, transmitting, to at least two n client devices, the generator g and the number p, receiving, from each client device i of the at least two client devices, a respective value k.sub.i=g.sup.ri mod p and transmitting the set of respective values k.sub.i to each client device i of the at least two client devices where respective added group of shares are generated on each client device i. The method includes receiving each respective added group of shares from each client device i of the at least two client devices and adding all the respective added group of shares to make a global sum of shares and dividing the global sum of shares by n.

A system and method for securely computing an inference of two types of tree-based models, namely XGBoost and Random Forest, using secure multi-party computation protocol. The method includes computing a respective comparison result of each respective node of a plurality of nodes in a tree classifier. Each node has a respective threshold value. The respective comparison result is based on respective data associated with a data owner device being applied to a respective node having the respective threshold value. The method includes computing, based on the respective comparison result, a leaf value associated with the tree classifier, generating a share of the leaf value and transmitting, to the data owner device, a share of the leaf value. The data owner device computes, using a secure multi-party computation and between the model owner device and the data owner device, the leaf value for the respective data of the data owner.