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 computer-implemented method for maintaining crypto tokens of a first type. A smart contract from a distributed ledger defines a bonding curve being as a sequence of multiple curve segments. A creation or annul function of the smart contract may integrate a bonding curve starting from the current supply size to a new supply size to determine an amount of crypto tokens of a second type. The crypto tokens of the second type may be transferred to or from a pool associated with the smart contract, while crypto tokens of the first type may be transferred to a user or may be destroyed. The current supply size of first type tokens may be updated correspondingly.

According to various embodiments, a method for code-based generation of a key pair for asymmetric cryptography is described including generating a private key defining a linear code, determining a parity check or generator matrix for the linear code, blinding a sub-matrix of the parity check or generator matrix, generating a blinded inverse matrix by inverting the blinded sub-matrix or by inverting a quadratic matrix contained in the blinded sub-matrix, de-blinding the blinded inverse matrix to generate an inverse matrix and generating a public key for the private key using the inverse matrix.

A method performs cryptographic operations on data in a processing device. An iterative operation between a first operand formed by a given number of words and a second operand using a secret key is performed. The iterative operation includes, for each bit of the secret key, applying one of a first set operations and a second set of operations to the first operand and to the second operand depending on of the bit, and conditionally swapping words of the first and the second operand based on a control bit value obtained by applying a logic XOR function to a random bit.

A method for key generation is arranged in a client processor device, by means of which a second public client key P.sub.c′ of the client is generated. The public key P.sub.c′ is formed by a calculation, or sequence of calculations, which does not contain any operation whose result depends exclusively on the nonce s and at least one public value, or the public key P.sub.c′ being formed by a calculation, or sequence of calculations, where into each operation in which the nonce s enters, at least one non-public value enters the first private client key k.sub.c or the second private client key k.sub.c′, for example as a result of the calculation P.sub.c′=(k.sub.c′.Math.s).Math.G+(k.sub.c′.Math.k.sub.c).Math.P.sub.t.

Various techniques are provided to implement information leakage mitigation associated with elliptic curve operations. In one example, a method includes generating second data based on first data. The first data is associated with a message. The second data is associated with a decoy message. The method further includes performing a first elliptic curve operation based on the first data. The method further includes performing a second elliptic curve operation based on the second data. The first elliptic curve operation and the second elliptic curve operation are performed in a random order. Related systems and devices are provided.

Aspects of the present disclosure involve techniques and cryptographic processors configured to perform the techniques that include sign-efficient addition and subtraction operations that use Montgomery reduction and are capable of facilitating fast streaming operations. The techniques involve receiving a first number and a second number, where the first number and second number are within a target interval, and performing a modular operation to obtain a third number, the third number being within the same target interval and representing a sum or a difference of a rescaled first number and a rescaled second number, and wherein the modular operation includes a Montgomery reduction.

Disclosed herein is a data storage device comprising a data path and an access controller. The data path comprises a data port configured to transmit data between a host computer and the data storage device. The data storage device is configured to register with the host computer as a block data storage device. A non-volatile storage medium stores encrypted user content data and a cryptography engine is connected between the data port and the storage medium and uses a cryptographic key to decrypt the encrypted user content data. The access controller generates a challenge for an authorized device; sends the challenge to the authorized device; receives a response to the challenge from the authorized device over the communication channel; calculates the cryptographic key based on the response; and provides the cryptographic key to the cryptography engine to decrypt the encrypted user content data stored on the storage medium.

A system for an artificial intelligence synchronized distributed ledger. The system includes a computing device containing a receiving module, the receiving module designed and configured to receive an input from a remote device, parse the input to identify protected and non-protected data contained within the input, transform the protected data into a digitally signed assertion and convert the non-protected into an encrypted datastore. The computing device containing a processing module, the processing module designed and configured to receive the digitally signed assertion from the receiving module, insert the digitally signed assertion into an immutable sequential data structure, receive the encrypted datastore, retrieve at least an input, generate a record utilizing the at least a retrieved input, and perform a first machine-learning process utilizing the at least a retrieved input.

Embodiments include a method of adding first and second binary numbers having C bits and divided into D words to provide a third binary number in E successive adding operations, C, D and E being plural positive integers, the method comprising: a first group of D adding operations adding together respective words of the first and second binary numbers to provide D sum and carry outputs ranging from a least significant to a most significant sum and carry output; one or more subsequent groups of adding operations adding together sum and carry outputs from an immediately preceding group of adding operations, a final group of the one or more subsequent groups resulting in the third binary number consisting of the sum outputs from the final group and a carry from the most significant carry output of the final group, wherein E is less than D.