Abstract: A method, apparatus and computer program product for privacy-preserving homomorphic inferencing. In response to receipt of encrypted data, a ciphertext of real numbers is generated. Each real number has an associated sign that is desired to be maintained. A mask is then identified, preferably via an iterative algorithm that works on a trial and error basis to locate an appropriate solution. The mask comprises set of values randomly distributed over a given positive range and that remain positive after encoding under a fixed-point arithmetic and with a low scale value. Under homomorphic encryption, the ciphertext is then multiplied by the mask to generate a result comprising values corresponding to the real numbers in the ciphertext and that maintain their associated signs. The result is provided as a response to the encrypted data.

Abstract: Mechanisms are provided for fully homomorphic encryption enabled graph embedding. An encrypted graph data structure, having encrypted entities and predicates, is received and, for each encrypted entity, a corresponding set of entity ciphertexts is generated based on an initial embedding of entity features. For each encrypted predicate, a corresponding predicate ciphertext is generated based on an initial embedding of predicate features. A machine learning process is iteratively executed, on the sets of entity ciphertexts and the predicate ciphertexts, to update embeddings of the entity features of the encrypted entities and update embeddings of predicate features of the encrypted predicates, to generate a computer model for embedding entities and predicates. A final embedding is output based on the updated embeddings of the entity features and predicate features of the computer model.

Abstract: A computer-implemented method including, in a fully-homomorphic encryption (FHE) scheme that employs ciphertext rescaling at different levels of a modulus chain of prime numbers: initializing a scale of the highest level of the modulus chain to a value that equals a weighted geometric mean of all the prime numbers, wherein, in the weighted geometric mean, the weight for each of the prime numbers is two to the power of: a location of the respective prime number in the modulus chain, minus one.

Abstract: A method comprising: receiving an input tensor having a shape defined by [n1, . . . , nk], where k is equal to a number of dimensions that characterize the input tensor; receiving tile tensor metadata comprising: a tile tensor shape defined by [t1, . . . , tk], and information indicative of an interleaving stride to be applied with respect to each dimension of the tile tensor; constructing an output tensor comprising a plurality of the tile tensors, by applying a packing algorithm which maps each element of the input tensor to at least one slot location of one of the plurality of tile tensors, based on the tile tensor shape and the interleaving stride, wherein the interleaving stride results in non-contiguous mapping of the elements of the input tensor, such that each of the tile tensors includes a subset of the elements of the input tensor which are spaced within the input tensor according to the interleaving stride.

Abstract: A computer-implemented method for generating hash values to determine string similarity is disclosed. The computer-implemented method includes converting a first text string of a first data set into a first set of shingles. The computer-implemented method further includes determining a weight associated with each shingle in the first set of shingles based, at least in part, on a particular record field associated with a shingle. The computer-implemented method further includes generating, based on a hash function, a hash value for each shingle in the first set of shingles. The computer-implemented method further includes reducing the hash value generated for each shingle in the first set of shingles, based, at least in part on the weight associated with the shingle.

Abstract: In response to identifying that a Single Instruction, Multiple Data (SIMD) operation has been instructed to be performed or has been performed by a Fully-Homomorphic Encryption (FHE) software on one or more original ciphertexts, performing the following steps: Performing the same operation on one or more original plaintexts, respectively, that are each a decrypted version of one of the one or more original ciphertexts. Decrypting a ciphertext resulting from the operation performed on the one or more original ciphertexts. Comparing the decrypted ciphertext with a plaintext resulting from the same operation performed on the one or more original plaintexts. Based on said comparison, performing at least one of: (a) determining an amount of noise caused by the operation, (b) determining whether unencrypted data underlying the one or more original ciphertexts has become corrupt by the operation, and (c) determining correctness of an algorithm which includes the operation.

Abstract: A computer-implemented method for privately determining data intersection is disclosed. The computer-implemented method includes performing private set intersection between two record sets to determine identical intersecting records corresponding to a particular record field. The computer-implemented method includes removing any identical intersecting records from each record set to form two record subsets. The computer-implemented method includes separately computing locality sensitive hash values for each of the two record subsets, wherein the locality sensitive hash values are computed for records corresponding to the particular record field. The computer-implemented method includes jointly performing private set intersection between the locality sensitive hash values separately computed for each of the two record subsets.

Abstract: A method, a neural network, and a computer program product are provided that optimize training of neural networks using homomorphic encrypted elements and dropout algorithms for regularization. The method includes receiving, via an input to the neural network, a training dataset containing samples that are encrypted using homomorphic encryption. The method also includes determining a packing formation and selecting a dropout technique during training of the neural network based on the packing technique. The method further includes starting with a first packing formation from the training dataset, inputting the first packing formation in an iterative or recursive manner into the neural network using the selected dropout technique, with a next packing formation from the training dataset acting as an initial input that is applied to the neural network for a next iteration, until a stopping metric is produced by the neural network.

Abstract: An example system includes a processor to receive, from a client device, a delete query requesting deletion of a row of in a fully homomorphically encrypted (FHE) database. The processor can store an identifier of the row to be deleted in a deletion queue, where the row is to be replaced with values of a row to be inserted from a received insertion query.

Abstract: In response to identifying that a Single Instruction, Multiple Data (SIMD) operation has been instructed to be performed or has been performed by a Fully-Homomorphic Encryption (FHE) software on one or more original ciphertexts, performing the following steps: Performing the same operation on one or more original plaintexts, respectively, that are each a decrypted version of one of the one or more original ciphertexts. Decrypting a ciphertext resulting from the operation performed on the one or more original ciphertexts. Comparing the decrypted ciphertext with a plaintext resulting from the same operation performed on the one or more original plaintexts. Based on said comparison, performing at least one of: (a) determining an amount of noise caused by the operation, (b) determining whether unencrypted data underlying the one or more original ciphertexts has become corrupt by the operation, and (c) determining correctness of an algorithm which includes the operation.

Abstract: An example system includes a processor to receive, from a client device, a delete query requesting deletion of a row of in a fully homomorphically encrypted (FHE) database. The processor can store an identifier of the row to be deleted in a deletion queue, where the row is to be replaced with values of a row to be inserted from a received insertion query.

Abstract: An example system includes a processor to receive a preprocessed query from a client device for a fully homomorphic encryption (FHE) encrypted database. The processor can execute the preprocessed query on the FHE encrypted database to generate a response. The processor can transmit a partially-processed response to the client device, which can post-process the query computation.

Abstract: A method comprising: receiving an input tensor having a shape defined by [n1, ...,nk], where k is equal to a number of dimensions that characterize the input tensor; receiving tile tensor metadata comprising: a tile tensor shape defined by [t1, ..., tk], and information indicative of an interleaving stride to be applied with respect to each dimension of the tile tensor; constructing an output tensor comprising a plurality of the tile tensors, by applying a packing algorithm which maps each element of the input tensor to at least one slot location of one of the plurality of tile tensors, based on the tile tensor shape and the interleaving stride, wherein the interleaving stride results in non-contiguous mapping of the elements of the input tensor, such that each of the tile tensors includes a subset of the elements of the input tensor which are spaced within the input tensor according to the interleaving stride.

Abstract: An example system includes a processor to receive, at a setup or sign-up, a first cipher including a biometric template transformed using a first transformation and encrypted using a secret key, a second cipher including a security vector encrypted using the secret key, a third cipher including the biometric template transformed using a second transformation and encrypted, and a fourth cipher including an encrypted second security vector. The processor can receive, at a runtime or sign-in, a fifth cipher and a sixth cipher. The processor can verify that the fifth cipher includes a second biometric template transformed using the first transformation and encrypted using the secret key and that the sixth cipher includes the second biometric template transformed using the second transformation by testing a format attribute of the transformation functions using comparisons of inner products.

Abstract: An efficient packing method that will optimize use of the homomorphic encryption ciphertext slots, trading-off size, latency, and throughput. Technology for working with tensors (multi-dimensional arrays) in a system that imposes tiles, that is, fixed-size vectors. An example of a system that imposes tiles are homomorphic encryption schemes, where each ciphertext encrypts a vector of some fixed size. The tensors are packed into tiles and then manipulated via operations on those tiles. Also, syntax for notation for describing packing details. This technology interprets the tiles as multi-dimensional arrays, and combines them to cover enough space to hold the tensor. An efficient summation algorithm can then sum over any dimension of this tile tensor construct that exists in the physical or logical addressing space of a computer data memory.

Abstract: An efficient packing method that will optimize use of the homomorphic encryption ciphertext slots, trading-off size, latency, and throughput. Technology for working with tensors (multi-dimensional arrays) in a system that imposes tiles, that is, fixed-size vectors. An example of a system that imposes tiles are homomorphic encryption schemes, where each ciphertext encrypts a vector of some fixed size. The tensors are packed into tiles and then manipulated via operations on those tiles. Also, syntax for notation for describing packing details. This technology interprets the tiles as multi-dimensional arrays, and combines them to cover enough space to hold the tensor. An efficient summation algorithm can then sum over any dimension of this tile tensor construct that exists in the physical or logical addressing space of a computer data memory.

Abstract: Embodiments may include techniques to prevent illegal ciphertexts using distance computations on homomorphic and/or functional encrypted templates while detecting whether the resulting distance does not meet requirements for validity.

Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.

Abstract: Embodiments may provide techniques to detect cyber-security events in IoT data traffic that provide improved detection accuracy and preservation of privacy.

Abstract: Embodiments may include techniques to prevent illegal ciphertexts using distance computations on homomorphic and/or functional encrypted templates while detecting whether the resulting distance does not meet requirements for validity.