International Business Machines Corporation

Gilad Ezov

Ehud Aharoni

Omri Soceanu

Hayim Shaul

Abigail Goldsteen

Ron Shmelkin

Allon Adir

Ariel Farkash

Micha Gideon Moffie

Nir Drucker

Lev Greenberg

Ramy Masalha

Fady Copty

Dov Murik

Evgeny Shindin

Ronen Levy

Muhammad Barham

Reducing homomorphic encryption (HE) rotations is provided. Input of a source tensor of HE ciphertexts is received and a mapping of elements from the source tensor to a target tensor. For each ciphertext, a vector of required rotations is computed according to the mapping plus a list of unique rotations. A first and second list of rotations are generated which have a combined number of rotations less than the list of unique rotations. For each rotation in the first list a ciphertext vector is computed that holds selected elements cyclically rotated by that rotation. For each rotation in the second list a subset of elements is selected from the ciphertext which is summed with ciphertext vectors generated according to the first list. A rotated ciphertext is generated from this sum rotated by the rotation in the second list. Rotated ciphertexts are summed, and the target tensor is output.

Reducing homomorphic encryption rotations when reshaping a ciphertext

An example system includes a processor to receive a ciphertext including an encrypted packed plaintext. The processor can, in response to detecting that a reshape is to be executed for a data structure including the packed ciphertext, identify a target shape for the data structure and execute the reshape based on the identified target shape. The reshape is executed using multiplication by an identity element. The processor can then execute a homomorphic computation using the reshaped data structure.

Reshaping data structures for homomorphic computations using multiplication by identity elements

Mechanisms are provided for performing a tournament selection process of a computer function. A request is received to execute the computer function on an input vector data structure, where a result of the computer function is provided by executing the tournament selection process. The input vector data structure is received, comprising a plurality of values where each value corresponds to a vector slot. An index vector data structure is received that comprises indices of the vector slots of the input vector. Iteration(s) of the tournament selection process are executed to identify a value in the input vector satisfying a criterion of the computer function. An operation is performed on the index vector data structure to generate an indicator vector data structure that uniquely identifies a slot in the input vector data structure that is a result of the computer function being executed on the input vector data structure.

Tournament type selection operations on encrypted data

A method, apparatus and a product for data generalization for predictive models. The method comprising: obtaining a training dataset that comprises a plurality of training instances and predicted labels thereof, wherein each training instance is a valuation of a set of features, wherein the set of features comprises a feature having a domain, wherein the predicted label of each training instance is a label predicted thereto by a predictive model; training an auxiliary model using the training dataset; based on the auxiliary model, determining an alternative set of features that is a generalization of the set of features, wherein the alternative set of features comprises a generalized feature having a generalized domain, wherein each value in the generalized domain corresponds to one or more values in the domain; obtaining a generalized instance having a valuation of the alternative set of features; and determining a label for the generalized instance.

Data generalization for predictive models

Embodiments may provide techniques that may provide more accurate and actionable alerts by cloud workload security systems so as to improve overall cloud workload security. For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise generating performance and security information relating to a software system during development of the software system, generating performance and security information relating to the software system during deployed operation of the software system, matching the performance and security information generated during development of the software system with the performance and security information generated during deployed operation of the software system to determine performance and security alerts to escalate, and reporting the escalated performance and security alerts.

Sharing insights between pre and post deployment to enhance cloud workload security

A method comprising: receiving an input tensor having a shape defined by [n, . . . , n], 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 [t, . . . , t], 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.

Efficient convolution in an environment that enforces tiles

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.

Analysis and debugging of fully-homomorphic encryption

An example system includes a processor that can obtain a circuit describing operations of sequential secure computation code. The processor can modify the circuit based on a cost function. The processor can partition the circuit into a number of sub-circuits. The processor can assign the number of the sub-circuits to different processors for execution.

Partitioning circuits for execution of sequential secure computation code on multiple processors

An example system includes a processor to receive training data and predictions on the training data of a trained machine learning model to be anonymized. The processor is to generate generalized data from training data based on the predictions of the trained machine learning model on the training data. The processor is to train an anonymized machine learning model using the generalized data.

Training anonymized machine learning models via generalized data generated using received trained machine learning models

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.

Packing arbitrary tensors into tiles with descriptive shapes

A method, apparatus and a product for data generalization for predictive models. The method comprising: based on a labeled dataset, determining a plurality of buckets, each of which has an associated label; determining a plurality of clusters, grouping similar instances in the same bucket; based on the plurality of clusters, determining an alternative set of features comprising a set of generalized features, wherein each generalized feature corresponds to a cluster of the plurality of clusters, wherein a generalized feature that corresponds to a cluster is indicative of the instance being mapped to the corresponding cluster; obtaining a second instance; determining a generalized second instance that comprises a valuation of the alternative set of features for the second instance; and based on the generalized second instance, determining a label for the second instance.

A method of limiting data usage for certified purposes by using functional encryption, comprising: receiving from a software publisher an application code and declared privacy information, the declared privacy information specifies at least one declared usage for at least one data type; analyzing the application's usage of data collected by the application, to identify an actual usage of the at least one data type by a function; identifying when the actual usage is compliant with the at least one declared usage according to the analysis; in response to the identification, creating a pair of a public key and a master private key; creating a function private key for the function using the master private key; and sending the function private key to the software publisher to be used for operating the function on data which is encrypted using the public key.

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Nesher, Israel

Gilad Ezov