Abstract: An example system includes a processor to pack a received tensor using a designated packing to generate a number of smaller ciphertexts. The processor can compute a rotation using the number of smaller ciphertexts to simulate a rotation operation on the tensor.

Abstract: An example system includes a processor that can receive a number of complex packed tensors, wherein each of the complex packed tensors include real numbers encoded as imaginary parts of complex numbers. The processor can execute a single instruction, multiple data (SIMD) operation on the complex packed tensors using an integrated circuit of real and complex packed tensors in a complex domain to generate a result.

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: An example system includes a processor to partition an arithmetic circuit representing a homomorphically encrypted (HE) code into a number of execution blocks. The processor can generate, for each of the number of execution blocks, manifests describing access patterns for a number of different machine environments. The processor can then dynamically execute the HE code by selecting successive blocks to execute based on an access pattern calculated for the execution block corresponding to a detected current machine environment.

Abstract: An example system includes a processor to prune a machine learning model based on an importance of neurons or weights. The processor is to further permute and pack remaining neurons or weights of the pruned machine learning model to reduce an amount of ciphertext computation under a selected constraint.

Abstract: An example system includes a processor to receive a machine learning network and a selected homomorphic encryption (HE) packing framework. The processor can generate list of HE packings for the machine learning network based on the selected HE packing framework. The processor can extend the machine learning network to include additional neurons based on the list of HE packings. The processor can also train the extended machine learning network.

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.