A first network device for use in a blockchain network is described. The first network device comprises means for, while carrying out a first iteration of a consensus protocol involving a second node device (103), in response to a trigger event (3040), obtaining a time interval function of a time interval start trigger event (3040) and of a time interval end trigger event (3080), each event being linked to a message of the consensus protocol and wherein at least the end trigger event (3080) comprises receiving a message from the second node device; means for transmitting (3083) first data (Delay_102) representative of said time interval to said second node device; means for, during a subsequent iteration of said consensus protocol involving said second node device, receiving a message (3140) from said second node device, said message containing second data (DelayTX_102) representative of said time interval from said second node device; means for authenticating said second node device as a function of said second data. A second network device, methods at the first and second network devices and a computer readable medium with code for carrying out the methods are also described.

A verification platform may include a data connection to receive a stream of industrial asset data, including a subset of the industrial asset data, from industrial asset sensors. The verification platform may store the subset of industrial asset data into a data store, the subset of industrial asset data being marked as invalid, and record a hash value associated with a compressed representation of the subset of industrial asset data combined with metadata in a secure, distributed ledger (e.g., associated with blockchain technology). The verification platform may then receive a transaction identifier from the secure, distributed ledger and mark the subset of industrial asset data in the data store as being valid after using the transaction identifier to verify that the recorded hash value matches a hash value of an independently created version of the compressed representation of the subset of industrial asset data combined with metadata.

There is provided a cryptographic key determination device for determining one or more cryptographic keys in a cryptographic device, the cryptographic device being configured to execute one or more test programs, the cryptographic device comprising one or more components (11-i), each component (11-i) being configured to generate static and dynamic data, the dynamic data being generated in response to the execution of the one or more test programs, wherein the cryptographic key determination device comprises: a data extraction unit configured to extract at least one part of the static data and at least one part of the dynamic data generated by the one or more components (11-i), and a key generator configured to combine the at least one part of static data and the at least one part of dynamic data, and to determine the one or more cryptographic keys by applying a cryptographic function to the combined data.

Techniques are described for securely managing computing resources in a computing environment comprising a computing service provider and a remote computing network. The remote computing network includes computing and network devices configured to extend computing resources of the computing service provider to remote users of the computing service provider. The network devices include a trusted network device that includes a root of trust. The trusted network device detects that a new device is communicatively coupled to a port on the trusted network device. The trusted network device determines that the new device is not authorized to access computing resources at the remote computing network. The port is isolated at the trusted network device.

Computer-implemented techniques for managing transactions of machine learning algorithm updates are described. In one embodiment, a computer-implemented is provided that comprises receiving, by a system operatively coupled to a processor, a request for an update to a machine learning model associated with a software program, wherein the request is received in accordance with a defined blockchain protocol, and wherein the request comprises model development data used in association with optimization of an instance of the machine learning model. The method further comprises, employing, by the system, a blockchain network to facilitate managing fulfillment of the request.

It is provided a method for controlling access to a physical space using a co-sign delegation. The method is performed in a lock device and comprises the steps of: receiving an access request from an electronic key; obtaining a plurality of delegations, wherein each delegation is a delegation from a delegator to a delegatee, the plurality of delegations collectively forming a chain of delegations; determining that a delegation is a co-sign delegation, indicating that all further delegations need to be cryptographically signed by both the delegator of the respective delegation and by an access controller; and granting access to the physical space when the chain of delegations start in an owner of the lock device and ends in the electronic key; and when all delegations in the chain of delegations after the co-sign delegation are cryptographically signed by both the delegator of the respective delegation and by the access controller.

Disclosed herein are methods, systems, and apparatus for processing blockchain-based guarantee information. One of the methods includes receiving a first cyphertext of a first digital document specifying a guarantee from a first computing device associated with at least a first guarantor and one or more zero-knowledge proofs (ZKPs) related to one or more values associated with the guarantee, and the first digital document specifies one or more predetermined conditions of executing the guarantee; verifying that the one or more ZKPs are correct; storing the first cyphertext to a blockchain based on performing a consensus algorithm; receiving a first message from a second computing device associated with a beneficiary or a representative of the beneficiary.

Systems and methods are disclosed for application data amalgamation through integration with third-party applications. A dynamic stub operates within a user interface application on a client computing device. The dynamic stub enables integration of functionality of an associated middleware system. The dynamic stub extracts data from a user interface of a third-party application system based on a set of data capture components. The dynamic stub transmits data to the middleware system. The middleware system transmits information back to the dynamic stub based on the data. The middleware system can reconfigure the dynamic stub in order to alter at least a portion of the user interface based on the information received from the middleware system.

In an example, a subject using a user mobile-identification-credential device (UMD) requests vetting by a vetting system, which receives verified subject information associated with a level-n mobile identification credential (MIC-n) that UMD received from a level-n authorizing party system (APS-n). MIC-n is linked to lower level MIC-0 to MIC-(n−1). The vetting system, as level-n relying party system (RPS-n), uses the verified subject information associated with the linked MIC-0 to MIC-n to verify or not verify the identity of the subject, develops an identity profile of the subject, and determines a vetting result of the subject by calculating a composite trust score based on MIC trust values for the multiple levels of MIC. MIC-i (i=1 to n) is linked to MIC-(i−1) which UMD received from APS-(i−1), and APS-i is RPS-(i−1) which verified the identity of the subject using verified subject information associated with MIC-(i−1), such that MIC-0 to MIC-n are linked.

The present disclosure aims to implement UAV (unmanned aerial vehicle) logistics operation and air traffic control in flyable airspace technically through a UAV task planning system, which depends on blockchain technology to carry out UAV air traffic surveillance on flight segments in a predetermined barrier-free airway and optimize air traffic according to a safe separation distance for fewest UAV operators, air traffic controllers, communications links and airborne loads.