An approach is provided for encrypting data. Using an encryption function, values of keys in a first database table are encrypted. The encryption function is determined to be homomorphic to sorting operators. A decryption function that decrypts the encrypted keys is determined to be homomorphic to sorting operators. Responsive to the encryption and decryption functions being determined to be homomorphic, a merge join operation is selected. The merge join operation operates on the first database table and a second database table and includes the decryption function in a joining condition. Using the merge join operation, an execution of a query is optimized. The query accesses one or more data items in the first or second database table.

Systems and methods are described based on an integrated circuit that performs a challenge-response physically unclonable function (PUF). The PUF is used for challenge-response authentication. The integrated circuit includes a PUP block configured to output an n-bit internal response corresponding to a challenge that requests a response where n is an integer greater than 1 and a response generator configured to calculate a Hamming weight of the internal response and output the response by comparing the Hamming weight with at least one reference.

Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Novel tools and techniques are provided for implementing signal encryption or signal authentication. In various embodiments, a second computing system might pack, using a packing function, two or more elements of a second vector associated with a third entity to generate a packed second vector; might individually encrypt, using a generated public key received from a first computing system, each element of the packed second vector to generate an encrypted packed second vector; might pack two or more elements of an encrypted first vector from the first computing system to generate a packed encrypted first vector; might combine the encrypted packed second vector with the packed encrypted first vector to generate a combined packed encrypted vector; and might send the combined packed encrypted vector to the first computing system for generating a similarity score that is indicative of differences between the second vector and the first vector.

In accordance with some embodiments, an apparatus for privacy protection is provided. The apparatus includes a random number generator providing a random number sequence. The apparatus also includes a key generator operable to receive the random number sequence and synthesize the random number sequence to generate a plurality of keys. The apparatus also includes a plurality of randomizing chains, each receiving a corresponding key from the key generator and providing a respective discrete random number sequence based on the corresponding key. The apparatus further includes output devices, each of which is connected to a respective randomizing chain to receive the respective discrete random number sequence and produces a respective output noise signal based on a function of the respective discrete random number sequence. The apparatus also includes interfaces mating the output devices with the input devices of a second device.

A method for executing an operation by a circuit, may include using a first mask set of mask parameters including a same number of occurrences of all possible values of a word of an input data in relation to a size thereof, using an input set including for each mask parameter in the first mask set a data obtained by applying XOR operations to the input data and to the mask parameter and providing an output set including all data resulting from the application of the operation to a data in the input set. The output data may be obtained by applying XOR operations to any of the data in the output set and to a respective second mask parameter in a second mask set including a same number of occurrences of all possible values of the second mask parameters in relation to a size of thereof.

An electronic point multiplication device (100) is provided for computing a point multiplication (kG) on an elliptic curve between a multiplier (k) and a base point (G) on the elliptic curve (E) for use in a cryptographic protocol. The device being arranged to compute from a first set of multiple joint encodings (A.sub.i) a blinded base multiplier (A, 131), and a second set of multiple joint encodings (B.sub.i) multiple blinded auxiliary multipliers (.sub.i, 136). The device performs obtains the point multiplication (141) (kG) of the multiplier (k) and the base point (G) by computing the point addition of the point multiplication of the blinded base multiplier and the base point on the elliptic curve, and the multiple point multiplications of a blinded auxiliary multiplier and an auxiliary point. The blinded base multiplier and auxiliary multipliers may be represented in a plain format during the performing of the elliptic curve arithmetic.

A computer-implemented method comprises: committing a transaction amount of a transaction with a commitment scheme to obtain a transaction commitment value, the commitment scheme comprising at least a transaction blinding factor; generating a first key of a symmetric key pair; encrypting a combination of the transaction blinding factor and the transaction amount t with the first key; and transmitting the transaction commitment value T and the encrypted combination to a recipient node associated with a recipient of the transaction for the recipient node to verify the transaction.

Described herein are systems and methods that prevent against fault injection attacks. In various embodiments this is accomplished by taking advantage of the fact that an attacker cannot utilize a result that has been faulted to recover a secret. By using infective computation, an error is propagated in a loop such that the faulted value will provide to the attacker no useful information or information from which useful information may be extracted. Faults from a fault attack will be so large that a relatively large number of bits will change. As a result, practically no secret information can be extracted by restoring bits.

An encryption circuit includes a pipelined encryption core having a plurality of round cores therein. The pipelined encryption core is configured to perform a real round operation on each of a plurality of pieces of input data received therein and generate encryption data from the input data using an encryption operation comprising the real round operation. An encryption controller is provided, which is coupled to the pipelined encryption core. The encryption controller is configured to control the pipelined encryption core so that at least one of the plurality of round cores performs a virtual round operation as part of the encryption operation. The pipelined encryption core is configured to perform a virtual encryption operation using at least one of: (i) dummy data, and (ii) a dummy encryption key.