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 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: 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, 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: 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.