Systems, methods, and computer-readable media for facilitating an authentication processing service are provided.

The present invention relates to a method and system of cybersecurity; and particularly relates to an encryption method and system on the basis of cognitive computing for xenomorphic cryptography or unusual form of cryptography; said method comprises generating a Functional Neural Network or KeyNode (KN) of the system by programming a chain of multiple nodes also called Artificial Mirror Neurons (AMN) based on captured information of reaction time and emotional response to a simple task; racing the nodes in the Functional Neural Network or KeyNode (KN) as an encryption device or cipher for the time of use; generating a password at the time of use based on the sum of intrinsic values of the nodes in the racing network at this time and adopting the generated password for authentication. The present invention can be applied to secure online and mobile communication especially at the dawn of 5G with generalization of open API lifestyle platforms so as to allow real-time identification for digital cryptocurrency payments and other public distributed ledger technology (DLT) mechanisms.

A wideband chaotic waveform that is rateless in that it may be modulated at virtually any rate and has a minimum of features introduced into the waveform. Further, the waveform provided may be operated below a signal to noise ratio wall to further enhance the LPD and LPE aspects, thereof. Additionally, the present disclosure may provide a mix of coherent and non-coherent processing techniques applied to signal samples to efficiently achieve coarse synchronization with a waveform that is faster, more efficient and more accurate than using time domain signal correlators alone.

Decoding a partially encrypted data stream may include receiving and scanning the partially encrypted data stream. Scanning the partially encrypted data stream may include identifying an encrypted portion sentinel in the partially encrypted data stream subsequent to a first portion, identifying an encrypted portion in the partially encrypted data stream subsequent to the encrypted portion sentinel, and generating a decrypted data portion by decrypting the encrypted portion. Decrypting the encrypted portion may include identifying an encrypted data portion in the encrypted portion, the encrypted data portion omitting an end encrypted portion sentinel, decrypting the encrypted data portion, and identifying an end encrypted portion sentinel in the encrypted portion subsequent to the encrypted data portion. Decoding the partially encrypted data stream may include including the decrypted data portion in the decrypted output data stream, and outputting the decrypted output data stream to a client device in the second network domain.

A system includes N-distant independent plasmonic graphene waveguides. The N-distant independent plasmonic graphene waveguides are used to generate an N-partite continuous variable entangled state.

A computer program product includes a non-volatile computer readable medium and non-transitory program instructions embodied therein, the program instructions being configured to be executable by a processor to cause the processor to perform various operations. The operations may include obtaining first encoded data that encodes information of a first party, obtaining second encoded data that encodes information of a second party, performing a forward mashup algorithm on the first encoded data and second encoded data to form a third data set, and providing the third data set to the first party as a mashup two-dimensional barcode.

Some embodiments are directed to an electronic cryptographic device configured to determine a cryptographic key. The cryptographic device has a physically unclonable function, a debiasing unit, and a key reconstruction unit. The PUF is configured to produce a first noisy bit string during an enrollment phase and a second noisy bit string during a reconstruction phase. The debiasing unit (120) is configured to determine debiasing data from the first noisy bit string during the enrollment phase. The debiasing data marks bits in the first noisy bit string as retained or discarded. The key reconstruction unit is configured to determine the cryptographic key from bits in the second noisy bit string marked as retained by the debiasing data, the cryptographic key being independent from bits in the second noisy bit string marked as discarded by the debiasing data.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums that provide for techniques for scrambling and/or updating meta-data that enable an efficient internal copyback operation. In some examples, improved data distribution techniques decouple the scrambling key from a physical address to allow for copyback operations while maintaining data distribution requirements across a memory device. The controller may generate a seed value that is used by a scrambling algorithm to scramble the host-data and meta-data prior to the data being written. The seed value is then encoded and written to the page with encoded versions of the scrambled user data and meta-data—the random seed is written without scrambling the random seed.

Hardware circuitry defines logic for both Sbox generation and inverse Sbox generation via generating a multiplicative inverse matrix as a truth table for data. The hardware circuitry receives input plain text to be encrypted. The hardware circuitry divides the input plain text to be encrypted. The hardware circuitry feeds multiplicative inverse values generated from the input plain text to a transformer module for performing affine to encrypt the plain text data. The hardware circuitry receives encrypted data to be decrypted. The hardware circuitry divides the encrypted data to be decrypted. The hardware circuitry feeds multiplicative inverse generated from the encrypted data to the transformer module for performing inverse affine to decrypt the encrypted data.

Devices, systems and methods for improving reliability and security of a memory system are described. An example method includes receiving a seed value and a data stream, generating, based on the seed and using a physical unclonable function (PUF) generator, a PUF data pattern, generating, based on the seed, a pseudo-random data pattern, performing a first logic operation on the PUF data pattern and the data stream to generate a result of the first logic operation as a first data sequence, and performing a second logic operation on the pseudo-random data pattern and a second data sequence that is based on the first data sequence to generate a result of the second logic operation as a third data sequence for storage on the memory system, wherein the PUF generator is selected at least in-part based on one or more physical characteristics of the memory system.