Provided is a highly practical cryptographic technology which is capable of being used when encryption and decryption are performed in a single data processing device and which can be said to be unbreakable, or close to unbreakable. A data processing device is configured to generate encrypted data by encrypting processing target data and record the generated encrypted data in a predetermined recording medium, and to decrypt the encrypted data recorded in the recording medium back into the processing target data. The processing target data is data of a text. Encryption is performed in units of plaintext split data generated by splitting the processing target data into pieces having a predetermined number of bits. The units of the splitting are equal to or shorter than a bit length of a code for identifying characters in the text.

A method performed by a user device is disclosed. The method comprising generating a secret and measuring a biometric template of a user operating the user device. The method then generates a plurality of secret shares of the secret and of the biometric template. The user device then transmits the secret shares of the secret and of the biometric template to a plurality of recovery devices. After, the user device may then initiate a recovery of the secret and measure a biometric measurement of the user. Data of the biometric measurement may be transmitted to the plurality of recovery devices, where the recovery devices perform a partial computation. The user device use the plurality of partial computations to determine a match between the biometric template and the biometric measurement. If the two biometrics match, the user device can reconstruct the secret using shares of the secret from the recovery devices.

Provided are a random number generation device and the like capable of calculating a high precision random number using a memory capacity selected irrespective of the precision of the random number. A random number calculation device is configured to generate first random numbers based on given number and specify, for the given number of second random numbers in a target numeric extent, bin range depending on the first random numbers based on frequency information representing cumulative frequency regarding a frequency of numeric extent including respective second random numbers among given numeric extents, the numeric extent being determined in accordance with a desirable precision.

The present invention provides an improved system and method for using cryptography to secure computer-implemented choice mechanisms. In several preferred embodiments, a process is provided for securing participants' submissions while simultaneously providing the capability of validating their submissions. This is referred to as a random permutation. In several other preferred embodiments, a process is provided for securing participants' advance instructions while simultaneously providing the capability of validating their advance instructions. This is referred to as a secure advance instruction. Applications include voting mechanisms, school choice mechanisms, and auction mechanisms.

A server determines an array [[addr]] indicating a storage destination of each piece of data, generates an array of concealed values, and connects the generated array to the array [[addr]] to determine an array [[addr′]]. The server generates a sort permutation [[σ.sub.1]] for the array, applies the sort permutation [[σ.sub.1]] to the array [[addr′]], and converts the array [[addr′]] into an array with a sequence composed of first Z elements set to [[i]] followed by α.sub.i elements set to [[B]]. The server generates a sort permutation [[σ.sub.2]] for the converted array [[addr′]], generates dummy data, imparts the generated dummy data to the concealed data sequence, applies the sort permutations [[σ.sub.1]] and [[σ.sub.2]] to the data array imparted with the dummy data, and generates, as a secret hash table, a data sequence obtained by deleting the last N pieces of data from the sorted data array.

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.

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.

Systems and methods for tokenization to support pseudonymization are provided herein. An example method includes receiving an input set, seeding a random number generator with one or more secret data, transposing the input set using a first random number/transposition parameter generated by the random number generator to create a transposed input set, transposing a token set using a second random number/transposition parameter generated by the random number generator to create a transposed token set, and generating a token by substituting transposed input set values with transposed token set values.

A process of linking a key to a component is disclosed herein. In various aspects, the key may be a password, hash, key, encryption key, decryption key, seed value, unlock code, or other alphanumeric identifier, and the component includes a computer in networked communication, and may further include a specific user of the computer. The process may include the step of identifying a component using environmental variables associated with the component, and the process step of forming a representation of the key unique to the component. The representation is tested to determine that the identified component is the source of the representation, in various aspects. Accordingly, the process may include the step of testing the representation against previous representations thereby determining the representation is not statistically duplicative of previous representations, and the process may include the step of testing the representation against possible representations from the component where the possible representations are unique to the component.

A method of encrypting data in a nonvolatile memory device (NVM) includes; programming data in selected memory cells, sensing the selected memory cells at a first time during a develop period to provide random data, sensing the selected memory cells at a second time during the develop period to provide main data, encrypting the main data using the random data to generate encrypted main data, and outputting the encrypted main data to an external circuit, wherein the randomness of the random data is based on a threshold voltage distribution of the selected memory cells.