Abstract:
In some aspects, a set of input elements is obtained, at a rectified linear unit-activated neuron of a neural network based, on input data at the neuron. A first group and a second group of input elements are generated based on the set of input elements. The first group and the second group of input elements are associated with first weight elements and second weight elements, respectively. A first value is generated based on the first group of input elements and the first weight elements. A second value is generated based on the second group of input elements and the second weight elements. A third value and a fourth value are respectively generated based on a first operation and a second operation on the first value and the second value. An output of the neuron is generated based on the third value and the fourth value.
Abstract:
There is provided a device for processing homomorphically encrypted data. The device includes: inter-line butterfly array blocks, each inter-line butterfly array block including inter-line modulus butterfly units, each inter-line modulus butterfly unit being configured to perform a modulus butterfly operation based on a computation pair of data points received corresponding to a pair of input data points at a same row of a matrix of input data points; intra-line butterfly array blocks, each intra-line butterfly array block including intra-line modulus butterfly units, each intra-line modulus butterfly unit being configured to perform a modulus butterfly operation based on a computation pair of data points received corresponding to a pair of input data points at a same column of the matrix of input data points; and a clock counter communicatively coupled to each inter-line butterfly array block and each intra-line butterfly array block, and configured to output a counter signal for controlling each inter-line butterfly array block and each intra-line butterfly array block to operate with single cycle initiation interval. The matrix of input data points includes columns of input data points, whereby parallel input data points derived from the homomorphically encrypted data are arranged into the columns of input data points. Furthermore, the inter-line butterfly array blocks and the intra-line butterfly array blocks are arranged in series to form a pipeline for processing the matrix of input data points.
Abstract:
An access control method, which may be applied in a cloud environment, is provided in various embodiments. The access control method includes: receiving from a user a request for access to a resource; determining a group access key related to the resource; determining a user key of the user; determining whether the group key is an integer multiple of the user key; and granting the user access to the resource if it is determined that the group access key is an integer multiple of the user key.
Abstract:
Efficient polynomial multiplication for Accelerated Fully Homomorphic Encryption (FHE). An efficient method for large integer and polynomial multiplication in a ring using negacyclic convolution and discrete Galois transform with arbitrary primes is described. The method is adapted to work with arbitrary primes that support Gaussian arithmetic. Dealing with non-Gaussian primes gives rise to another problem of how to find primitive roots of unity and of (i). An efficient solution to find those roots of interest is provided.
Abstract:
There is provided a method of generating a ciphertext. The method includes encrypting an input data to produce an encrypted data, and randomizing the encrypted data to produce the ciphertext. In particular, the randomizing process includes performing an exclusive-or (xor) operation on the encrypted data with a cipher pad, whereby the cipher pad is generated based on an xor-homomorphic function of a first key using a second key generated based on the encrypted data. There is also provided a corresponding system for generating a ciphertext, a corresponding method and system for decrypting a ciphertext, and a corresponding method and system for searching ciphertexts in a database, such as at an untrusted server.
Abstract:
In some aspects, a method for generating encoded plaintext data in a plaintext vector space includes obtaining a plurality of vectors of plaintext elements, where each plaintext element is an element of a first finite field. The method further includes encoding the plurality of vectors of plaintext elements to a vector of field elements, where each vector of plaintext elements is encoded to a respective field element of the vector of field elements, each of the field elements is an element of a second finite field, and the second finite field is a finite extension field of the first finite field. The method additionally includes encoding the vector of field elements into an element of the plaintext vector space to produce the encoded plaintext data for homomorphic encryption and computation.
Abstract:
There is provided a method of privacy-preserving logistic regression training based on homomorphically encrypted ciphertexts. The method includes: obtaining a first packed ciphertext comprising at least a portion of a first training data sample packed into a first vector of slots thereof for training a privacy-preserving logistic regression model; obtaining a second packed ciphertext comprising a plurality of weights of the privacy-preserving logistic regression model packed into a first vector of slots thereof; determining at least a first output probability of the privacy-preserving logistic regression model based on the first packed ciphertext and the second packed ciphertext; and updating the plurality of weights based on the first output probability. There is also provided a corresponding system for privacy-preserving logistic regression training based on homomorphically encrypted data.