Embodiments are directed towards classifying data. A machine learning (ML) engine may select an ML model that may employ a cryptographic multi-party computation (MPC) protocol based on model preferences, including a parameter model, provided by a client. A randomness engine may be employed to provide random values and other random values based on the MPC protocol such that the random values may be provided to the client and the other random values may be provided to an answer engine. Input values that correspond to fields in the parameter model may be provided by the client such that the input values may be based on the MPC protocol and the random values. The answer engine may be employed to provide partial results to the question based on the ML model, the input values, and the MPC protocol that may be provided to the client.

Partially homomorphic encryption systems may be transformed into fully homomorphic encryption systems that are scalable, rapid in translation speed, difficult to invert or break, capable of enabling various types of public and/or private key generation protocols and semantically secure. Input plaintext data are transformed into modified plaintext data using a prime number operation and the modified plaintext data is then encrypted using any number of conventional encryption schemes. Desired computations on the encrypted data are transformed into homomorphic operations, based on the nature of the encryption format, and the homomorphic operations are applied to yield manipulated encrypted data. The manipulated encrypted data may be decrypted and the decrypted plaintext data may be modified into final, output plaintext data using a similar prime number operation as applied during encryption. The final, output plaintext is equivalent to plaintext data that would have been generated by just applying the desired computations to the input plaintext data.

Generally discussed herein are systems, devices, and methods for entwined encryption and error correction and/or error detection. An entwined cryptographic encode device can include a memory including data indicating a set of relatively prime, irreducible polynomials stored and indexed thereon, entwined encryption encoding circuitry to receive data, transform the data to a set of data integers modulo respective polynomial integers representative of respective polynomials of the polynomials stored on the memory, and perform a Da Yen weave on the transformed data based on received cipher data, and provide the weaved transformed data to a medium.

To perform high-speed and efficient processing of determining a correlation between vectors. An information processing system includes: a first information processing device having k number of vectors including secure data as an element; and a second information processing device having m number of vectors including secure data as an element. The first information processing device receives vector information regarding a vector Y selected from the m number of vectors, as encrypted data, from the second information processing device. A data processing unit of the first information processing device sequentially calculates element-based sample identifiers each having a specific common value for each of a plurality of different vectors Y selected from the m number of vectors to one vector X selected from the k number of vectors retained by the first information processing device, and sequentially determines the correlation between the one vector X and each of the plurality of different vectors.

A privacy preserving computation protocol for data analytics is described. The protocol includes a method for privacy-preserving computation of aggregated private data of a group of client devices wherein the method comprises: a server selecting at least t client devices from the group of client devices, each client device in the group: being identifiable by client index i; comprising an encryption function; being provided with key information including an encryption key e and a decryption key of a homomorphic threshold cryptosystem; generating or being provided with an random value r.sub.i and having access to or being provided with the random values of the other client devices in the group; the server transmitting client information to each selected client device, the client information including client indices identifying the selected client devices, the client information signalling a client device that the server would like aggregate encrypted private data of each of the selected client devices; the server receiving randomized encrypted private data and an associated decryption share from each selected client device, the decryption shares being configured such that decryption key d can be reconstructed on the basis of t decryption shares; and, the server aggregating, preferably summing or adding, the received randomized encrypted private data of the selected client devices using the homomorphic properties of the cryptosystem and using the decryption shares for decrypting the aggregated randomized encrypted private data into cleartext.

Provided is a pre-calculation device capable of keeping a secret against malicious behaviors of participants while keeping a processing load small. A Beaver triple generation processor generates a secret-shared Beaver triple formed of two secret-shared random numbers and a secret-shared value of a product of the two random numbers. A Beaver triple random inspection processor randomly selects a secret-shared Beaver triple, restores the Beaver triple through communication to and from other pre-calculation devices, and confirms that a product of first two elements is equal to a third element. The Beaver triple position stirring processor randomly replaces Beaver triples that have not been restored, to generate replaced secret-shared Beaver triples.

The disclosure herein relates to private set intersection techniques. The described private set intersection techniques enable entities to determine common data strings in their respective data sets. The private set intersection techniques described herein allow those common data strings to be shareable between the entities, while maintaining the secrecy of other data strings stored in their respective data sets.

To achieve high-speed and efficient parameter calculation processing of a logistic regression model. A logistic regression parameter is calculated, the logistic regression parameter being a parameter of the logistic regression model indicating the relationship between an explanatory variable and an outcome variable being secure data corresponding to each sample. A data processing unit calculates the inner product (t_s) of the explanatory variable and the outcome variable with application of secure computation being computation processing applied with converted data of each of the variables, and performs computation processing excluding the calculation processing of the inner product, as computation processing without the converted data, to calculate the logistic regression parameter in accordance with the maximum likelihood method with the Newton-Raphson method (iterative convergence method).

Provided is a data distribution method for a data distribution system which includes a device and a plurality of authentication servers. The data distribution method includes: receiving, by a first authentication server included in the plurality of authentication servers, transaction data including encrypted history information which is history information of the device encrypted using a secure computation method which enables computation without decrypting the encrypted history information; recording, by the first authentication server, the transaction data in a distributed ledger in synchronization with the plurality of authentication servers excluding the first authentication server, when a validity of the transaction data received from the device is verified by the first authentication server; and performing, by the first authentication server, secure computation on the encrypted history information included in the transaction data, the secure computation being computation processing performed without decrypting the encrypted history information.

Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.