3 results about "Fuzzy extractor" patented technology

A mutual authentication protocol based on radio frequency (RF) fingerprint and fuzzy extractor is provided. Two kinds of nodes in the protocol are denoted by authenticator and verifier respectively. In the registration phase, the verifier sends a registration request to the verifier, the verifier receives its RF fingerprint and uses the fuzzy extractor to process it. After storing the help string P related to the verifier, the key generated by R is returned to the verifier, and the verifier stores the key after receiving it. In the authentication phase, the verifier sends an encrypted message containing the challenge value to the verifier. After receiving it, the verifier recovers the key needed for decryption through the fuzzy extractor using the extracted RF fingerprint and the previously stored P value, and returns a reply message to the verifier to achieve the final two-way authentication effect.

The application discloses a kind of centerless hardware trust identification generation and authentication method based on PUF, belong to information security technical field.The application relies on the physical unclonable feature of PUF chip, and PUF feature drift is handled by combining error correction code and auxiliary data processing fuzzy extractor, and stable hardware native fingerprint is extracted;Stable characteristics are mixed with double salt factor, and local trusted UID is generated by SM3 algorithm of national secret;Based on UID and PUF feature, unique SM2 key pair is derived by HKDF-SM3 algorithm and validity is verified;Three-level layered key system is constructed, and key derivation is completed using SM4-CBC mode;Trusted UID, dynamic data are combined to complete SM2 signature of national secret, and one-time two-dimensional code is generated to realize double offline authentication;Centerless public key synchronization and authentication result traceability are realized by combining distributed hash table and alliance chain, without central control and special medium, native compatible national secret algorithm, solve the core problem of PUF stability and centerless public key distribution, realize the real centerless hardware trust authentication.

The application relates to network security and identity authentication, and provides a privacy protection identity authentication method and system based on zero-knowledge proof. A convolutional neural network is trained through a standard face data set to obtain a face detection, alignment and binary feature extraction model; a fuzzy extractor is used to convert high-dimensional binary features into high-entropy key shares and auxiliary data, and the high-entropy key shares and the auxiliary data are secret shared with a random key of a client. During authentication, the client restores the key shares, executes threshold Schnorr signature generation challenge signature with no less than t-1 signature nodes, and completes authentication after verification of the server. The scheme does not need to store original biological feature data, and realizes decentralized, multi-factor, high-security and anti-single-point-failure identity authentication.