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.

A distributed system that implements an online exchange may comprise a plurality of server nodes, each of which being configured to receive exchange transaction proposals from customers of the online exchange over a computer network and each being configured to store a copy of a blockchain distributed ledger of completed exchange transactions. A consensus engine may be coupled, over the computer network, to the plurality of server nodes and may receive a plurality of exchange transaction proposals from the plurality of server nodes. The consensus engine may be being further configured to achieve consensus on the plurality of exchange transaction proposals and to generate, in response, an ordering of agreed-upon exchange transaction proposals that includes the plurality of exchange transaction proposals on which consensus has been reached. This ordering of agreed-upon exchange transaction proposals is identically provided to each of the server nodes and specifies the order in which the server nodes are to execute exchange transactions and to update their copy of the distributed ledger. The ordering of agreed-upon exchange transaction proposals may optionally be re-ordered and identically provided to each server node to conform to the local orderings at the exchange transaction proposal's node server of origin.

A system for implementing mixed protocol certificates, the system includes a subject device designed and configured to receive, from an issuing device, a first digital certificate, wherein the first digital certificate further comprises a first digital signature public and private key pair according to a first digital signature protocol and a second digital signature public key according to a second digital signature protocol, wherein the second digital signature protocol is distinct from the first digital signature protocol, to generate a second digital certificate, wherein generating the second digital certificate comprises generating a subject digital signature signing the certificate, the subject digital signature generated as a function of the second digital signature protocol and to provide the first digital certificate and the second digital certificate to a verifying device.

A method for ensuring precedence for the processing of a blockchain transaction to prevent loss of cryptographic currency includes receiving a new blockchain transaction by a blockchain node in a blockchain network, confirming the new blockchain transaction including identifying a precedence transaction that was previously conducted and stored in the blockchain that involves both blockchain wallets included in the new blockchain transaction, including the new blockchain transaction in a new block that is generated, and distributing the new block to additional nodes in the blockchain network for confirmation and addition to the blockchain.

A method of reducing a data volume of a data stream in a surgical robotic system, the surgical robotic system comprising a robot having a base and an arm extending from the base to an attachment for an instrument, the arm comprising a plurality of joints whereby the configuration of the arm can be altered, the method comprising: receiving a data stream captured by the surgical robotic system, the data stream comprising data relating to a surgical procedure and having a first data volume; identifying a feature in the received data stream indicative of an event in the surgical procedure; and modifying, in dependence on the identified feature, the received data stream to generate a modified data stream having a second data volume that is smaller than the first data volume.

A computer-implemented method for submitting feedback for an entity to a blockchain is disclosed. The method, which is implemented at one of a plurality of participating nodes, includes: obtaining a first key, the first key being one of a fixed set of keys distributed to participating nodes that are eligible to submit feedback for the entity; generating first feedback (rj) of the entity for submission to the blockchain; encrypting the first feedback (rj) using at least the first key; and submitting the encrypted first feedback to a mixing service, the mixing service being configured to generate a mixed transaction based on the encrypted first feedback and at least one other encrypted feedback submission from one or more eligible participating nodes.

Surgical hub systems are disclosed. A surgical hub system comprises a surgical hub configured to communicably couple to a modular device comprising a sensor configured to detect data associated with the modular device and a device processor. The surgical hub comprises a hub processor, a hub memory coupled to the hub processor. The surgical hub system also comprises a distributed control system executable at least in part by each of the device processor and the hub processor. The distributed control system is configured to: receive the data detected by the sensor; determine control adjustments for the modular device according to the data; and control the modular device according to the control adjustments. When in a first mode, the distributed control system is executed by both the hub processor and the device processor. In a second mode, the distributed control system is executed solely by the device processor.

The present invention relates, in general, to computing engineering and, more particularly, to a method and system for anonymously identifying a user as a member of a group of users. The authors provide the improved anonymous identification witness hiding protocol intended to verify membership in a local community of registered participants based on one-way accumulators developed using quasi-commutative one-way elliptic curve functions. The identification protocol according to the present invention provides the required level of cryptographic security with low operational efforts and resource consumption.

Methods of operating a user equipment (UE) in a mobile communication network are disclosed. An authentication process start message may be transmitted from the UE to the mobile communication network, wherein the authentication process start message includes an identifier for the UE. After transmitting the authentication process start message from the UE, a request commit message may be received from the mobile communication network. Responsive to receiving the request commit message, a response commit message may be transmitted to the mobile communication network. After transmitting the response commit message, an authentication challenge message may be received corresponding to the authentication process start message. Related methods of operating network nodes are also discussed.

A disease development risk prediction system 10 includes: a data generation means 11 which generates combination data by combining at least two different types of receipt data using a combination key, wherein the receipt data includes an insured person number for an insured person which was converted using a predetermined method, a birth date or birth year and month which are both age-identifiable items, and gender, and the combination key combines the converted insured person number, age-identifiable items, and gender; and a model generation means 12 which uses the generated combination data to generate a prediction model predicting a risk of the insured person of developing a predetermined disease.