International Business Machines Corporation

Eyal Kushnir

Ehud Aharoni

Allon Adir

Omri Soceanu

Hayim Shaul

Guy Moshkowich

Nir Drucker

Ramy Masalha

Heiko Hary Ludwig

Nathalie Baracaldo Angel

Ronen Levy

Runhua Xu

Michael Mirkin

A technique for privacy-preserving homomorphic inferencing using a neural network having an activation function, such as a non-linear high-degree polynomial. The network is trained to learn input features of an input feature vector together with their associated inverses. During inferencing, an encrypted data point is received at the network. The data point comprises an input feature vector that has been extended with a set of one or more additional feature values, the set of one or more additional feature values having been generated by applying a normalized inverse function to respective one or more features in the feature vector. Homomorphic inferencing is performed on the encrypted data point using the machine learning model to generate an encrypted result, which is then returned. By applying the normalized inverse function, the high-degree polynomial can use any value of an input feature during inferencing, whether the value is within or outside of a particular input range.

Non-linear approximation robust to input range of homomorphic encryption analytics

Mechanisms are provided for performing a tournament-league comparison process of a computer function. A request is received to execute the computer function on a vector data structure, where a result of the computer function is provided by executing the tournament-league comparison process. The vector data structure comprises a plurality of values where each value corresponds to a vector slot. At least one iteration of a tournament comparison operation is executed to generate a first intermediate ciphertext and indicator matrix, where the first intermediate ciphertext comprises fewer vector slots than the at least one input vector data structure. A plurality of iterations of a league comparison operation are executed based on the first intermediate ciphertext and one or more second intermediate ciphertexts generated at each iteration of the league comparison operation. A final iteration of the league comparison operation is executed that outputs a final result of the tournament-league comparison process.

Tournament-league mechanisms for comparison based computer operations

Mechanisms are provided for performing a fully homomorphic encryption operation. The mechanisms generate, for a data set in a backend data store, a tree data structure comprising a hierarchy of nodes and edges connecting the nodes in a parent-child relationship. In response to receiving an encrypted query from a client computing device, a search operation is executed using the tree data structure at least by executing a copy-and-recurse computing tool to identify a portion of the tree data structure to which to apply a fully homomorphic encryption (FHE) operation. The copy-and-recurse computing tool copies a subset of nodes of the tree data structure and recurses the search operation into the copied subset of nodes. The FHE operation is executed on a portion of the data set, corresponding to the identified portion of the tree data structure, to generate results of the FHE operation which are then output.

Copy-and-recurse operations for fully homomorphic encrypted database query processing

An example system includes a processor to mask a ciphertext using four random elements to generate masked ciphertexts. The processor can send the masked ciphertexts to a server device. The processor can receive masked plaintexts from the server device. The processor can unmask the masked plaintexts using the four random elements to generate unmasked plaintexts.

Masking ciphertexts using random elements for secure decryption

A computer-implemented method for identifying a leaf in a tree is provided. A processor set receives a first point representing one or more values within a d-dimensional space for identifying the leaf in the tree. The processor set generates a parameter for identifying the leaf in the tree. The processor set identifies a node at highest depth of the tree. The processor set determines a first value of the parameter for generating a range. The processor set selects a subset of child nodes from the number of child nodes associated with the node at the highest depth of the tree based on the range. The processor sets the subset of child nodes as the node at the highest depth of the tree. The processor set traverses the tree by repeating the identifying step, the determining step, the selecting step, and setting steps until the leaf in the tree is identified.

Parametric searching under fully homomorphic encryption

An example system includes a processor to compute a tensor of indicators indicating a presence of partial sums in an encrypted vector of indicators. The processor can also securely reorder an encrypted array based on the computed tensor of indicators to generate a reordered encrypted array.

Secure reordering using tensor of indicators

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.

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Eyal Kushnir