Textual masking for multiparty computation is provided. The method comprises receiving masked input data from a number of contributors, wherein the input data from each contributor has a unique contributor mask value. A unique analyst mask factor is received for each contributor, computed by an analyst as a difference between a uniform analyst mask value and the contributor mask value. An API call is received from the analyst to aggregate the input data from the contributors. The respective analyst mask factors are added to the input data from the contributors, and the data is aggregated and shuffled. Computational results received from the analyst based on the aggregated input data are published. In response to API calls from the contributors, the analyst mask factors are removed from the computational results, wherein computational results received by each contributor are masked only by the respective contributor mask value.

A random flux of rising bubbles generates a fluctuating electrical current that is processed into a high-quality bit stream. Any stream of fully or mildly randomized bits is measured for its degree of randomness, and that randomness is amplified or attenuated as the case may be. Impact on ciphers and cyber security tools that rely on randomness.

A non-interactive protocol is provided for evaluating machine learning models such as decision trees. A client can delegate the evaluation of a machine learning model such as a decision tree to a server by sending an encrypted input and receiving only the encryption of the result. The inputs can be encoded as vector of integers using their binary representation. The server can then evaluate the machine learning model using a homomorphic arithmetic circuit. The homomorphic arithmetic circuit provides an implementation that requires fewer multiplications than a Boolean comparison circuit. Efficient data representations are then combined with different algorithmic optimizations to keep the computational overhead and the communication cost low. Related apparatus, systems, techniques and articles are also described.

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.

Example systems and methods for biometric authentication that can bridge fuzzy extractors with deep learning and achieve the goals of preserving privacy and providing recoverability from zero are disclosed. Embeddings comprising a face or speaker embedding in a non-Hamming distance space can be processed to create a personal reliable bit map and a reliable locality-sensitive hash (LSH) for mapping the non-Hamming distance space to a Hamming distance space. A fuzzy extractor can be applied to create metadata that can be stored on a computing device. A secret can be recovered from the metadata and can be used for identification.

A digital processing method, which comprises obtaining a stream of N-bit input data words; obtaining a value k between 0 and M1, inclusively, where M>1; processing each of the N-bit input data words at least based on the kth of M permutation elements to produce a corresponding N-bit output data word; and outputting a stream of N-bit output data words on a network or storing the stream of the N-bit output data words in a non-transitory storage medium.

A method for generating secure alternative representation for a numerical datum, being performed in a processing system comprising a processing unit coupled to a storage unit, is provide. The method comprises: receiving the numerical datum; providing a plurality of semi-finished conditions; associating each of the semi-finished conditions with one or more secret parameters to form a plurality of secret conditions; for each of the secret conditions: determining whether the numerical datum satisfies the secret condition; outputting a first character as a result element if the numerical datum satisfies the secret condition; and outputting a second character as the result element if the numerical datum does not satisfy the secret condition; and concatenating each result element being output corresponding to the secret conditions as an alternative representation for the numerical datum.

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

Systems, and associated methods, involving both a trusted and an untrusted device where sensitive data or keys are shared between those devices are disclosed. A disclosed method includes storing a key in a secure memory on a first device, receiving sensitive data via a user interface on a second device, generating a set of white box encryption instructions based on the key using a white box encryption generator on the first device, generating a complete data representation of the set of white box encryption instructions using a secure processor on the first device, transmitting the complete data representation from the first device to the second device, and encrypting the sensitive data using the complete data representation on the second device. The complete data representation is not Turing complete and is not executable with respect to the second device.

A digital processing method, which comprises obtaining a stream of N-bit input data words; obtaining a value k between 0 and M1, inclusively, where M>1; processing each of the N-bit input data words at least based on the kth of M permutation elements to produce a corresponding N-bit output data word; and outputting a stream of N-bit output data words on a network or storing the stream of the N-bit output data words in a non-transitory storage medium.