In one example, a first wireless device transmits one or more electronic keys, and a second wireless device receives and stores the electronic key(s) in a memory. A server or a user device uploads, receives or synchronizes the electronic key(s) from the second wireless device. In another example, one or more electronic keys are transmitted using a first wireless device, the electronic key(s) are received and stored in a memory of a second wireless device, and the electronic key(s) or other data are transmitted, uploaded or synchronized to a server or a user device. In another example, a device comprises: a wireless transceiver; a memory; and a processor communicably coupled to the wireless transceiver and the memory, wherein the processor receives one or more electronic keys from one or more wireless devices, and stores the electronic key(s) in the memory.

Provided are a method for training a model based on homomorphic encryption, a device, and a storage medium. The specific implementation is: acquiring homomorphic encrypted data in a model training process; determining a hyperparameter of a model approximation function according to state data present in the model training process, where the model approximation function is used for replacing a model original function involved in the model training process; and inputting the homomorphic encrypted data to the model approximation function for calculation, and performing model training according to a calculation result. Therefore, the application flexibility of functions is improved while achieving the protection of data privacy in the model training process.

A user of a blockchain network may obtain credentials for the user from an issuer, the credentials based on one or more attributes of the user, wherein the issuer is selected from one or more authorized issuers, and wherein the credentials include a signature on the one or more attributes and a secret key; generate an operation composed of a payload and a second signature; compute a commitment to a public key of the issuer; prove, using a one-out-of-many proof, that the commitment is a valid commitment to a public key of one of the authorized issuers; prove, using a zero-knowledge proof, proof of knowledge of the signature and the credentials under the public key of the issuer; and prove, using a proof of knowledge, of values of the signed secret key and attributes.

A method at a network element, the method including receiving at least one message at the network element, the at least one message being one or both of: an update status information message from an updates server; and an anomaly detection status information message from anomaly detection server; determining, based on the receiving the at least one message, a dynamic cybersecurity posture indication for an intelligent transportation system entity; and providing the dynamic cybersecurity posture indication for the intelligent transportation system entity to an Enrolment Authority, wherein the dynamic cybersecurity posture indication can be included in a certificate relating to the intelligent transportation system entity.

The present invention relates to a method, for a provider entity belonging to a provider group, to authenticate its belonging to an attribute provider group to a verification entity in a non-traceable manner without necessitating to share secret or large constants compromising privacy. Both entities comprise at least one attribute group arborescence, this attribute group arborescence being shared by the provider entity and the verification entity when the provider entity has the attribute. According to the invention, when a verification is triggered, the verification entity calculates a certificate from the attribute group arborescence, said certificate being calculated from the authentication tokens of the groups along the arborescence from the attribute verification group's token to the consumer group's token.

Disclosed are an electronic device for obfuscating user data and a server for decoding the same. A method for controlling an electronic device according to the present disclosure comprises the steps of: acquiring a security parameter according to data transmitted to an external server; applying an obfuscation algorithm to the data by using the security parameter; and transmitting the data, to which the obfuscation algorithm has been applied, to the external server. Furthermore, in connection with a method for controlling a system comprising an electronic device for obfuscating data and a server for decoding the same according to the present disclosure, a method for controlling the electronic device comprises the steps of: inserting a fingerprint into data; generating multiple pieces of split data having a preset first size on the basis of the data into which the fingerprint has been inserted; applying an obfuscation algorithm to one piece of split data selected from the multiple pieces of split data by using a preset security parameter; and transmitting the split data, to which the obfuscation algorithm has been applied, to the server. In addition, a method for controlling the server comprises the steps of: receiving the multiple pieces of split data, to which the obfuscation algorithm has been applied, from the electronic device; acquiring at least one piece of candidate data on the basis of the received multiple pieces of split data, to which the obfuscation algorithm has been applied; and acquiring data comprising the fingerprint among the at least one piece of candidate data.

A temporary EID (TEID) is generated based on an indicator of a hash algorithm, a nonce, and a hash generated using the hash algorithm. The hash is generated based on the indicator, nonce, and EID of a mobile device. The TEID is sent to the mobile network operator to identify the mobile device in lieu of using the device's EID. The TEID is stored in a data store and an eSIM profile for the mobile device is associated the TEID. The mobile device sends to an eSIM server the device's EID over a secure communications channel. The eSIM server generates a hash using the indicator and nonce contained in the stored TEID and the EID of the mobile device. The eSIM server verifies that the generated hash matches the hash contained in the TEID stored in the data store. If the hash matches, the eSIM server sends, to the mobile device, subscription credentials for accessing the mobile network in accordance with the data plan.

The present disclosure relates to a trustworthy data exchange. Embodiments include receiving, from a device, a query, wherein the query comprises a question. Embodiments include identifying particular information related to the query. Embodiments include receiving credentials from a user for retrieving the particular information related to the query. Embodiments include retrieving, using the credentials, the particular information related to the query from one or more data repositories that are part of a distributed database comprising an immutable data store that maintains a verifiable history of changes to information stored in the distributed database. Embodiments include determining, based on the particular information related to the query, an answer to the query. Embodiments include providing the answer to the device.

A method for detecting and tracking tainted cryptographic wallets. The method measures a wallet's propensity to engage in criminal or suspicious activity. Naturally, transacting with a criminal is tantamount either to funding crime or laundering its proceeds, so it is in our collective interest to identify—and then monitor or quarantine—any wallet with criminal association. The method also automatically flags risky withdrawal requests in real-time for further review before committing them to the blockchain. In some embodiments, the exchange can quarantine wallets at a certain AddressScore.

An approach is provided for managing pseudonymous or anonymous user data and relevant data management requests. The approach involves, for example, converting a numerical feature of a data point into a categorical form. The categorical form represents a value range into which a numerical value of the numerical feature falls. The approach also involves determining an identifier of a data contributor associated with the data point. The approach further involves concatenating the identifier with the categorical form. The approach further involves cryptographically hashing the identifier concatenated with the categorical form to generate a mark. The approach further involves associating the mark with the data point to generate marked pseudonymous-anonymous data. The approach further involves transmitting the pseudonymous-anonymous data to a data platform.