Abstract: Provided are a computer implemented method, system, and computer program product for determining vulnerability of computational resources to attack vectors. A potential attack vector is detected that is directed to a targeted resource of the computational resources that matches a known attack vector of a plurality of known attack vectors. Attributes of attack vectors directed to the targeted resource are processed, including the attributes of the known attack vector, to generate a vulnerability score for the targeted resource indicating a likelihood the targeted resource is exposed to a malicious attack. A lower vulnerability score indicates the targeted resource is less susceptible to the malicious attack than a higher vulnerability score. An alert is generated in response to determining that the generated vulnerability score indicates an increased vulnerability to the malicious attack.

Abstract: Mechanisms are provided for optimizing a deep learning (DL) computer model for homomorphic encryption (HE) workload processing. The mechanisms receive an original DL computer model architecture that is to be optimized for HE workload processing, and modifying the original DL computer model architecture by replacing a self-attention layer of the original DL computer model with an HE friendly self-attention layer that comprises a Power SoftMax function that does not have exponent terms, to thereby generate a modified DL computer model architecture. The mechanisms execute a machine learning training of the modified DL computer model architecture, approximate one or more elements of the Power SoftMax function with polynomials to generate a trained HE optimized DL computer model, and output the trained HE optimized DL computer model for execution on HE workloads.

Abstract: A technique to remotely identify potential compromise of a service provider that performs homomorphic inferencing on a model. For a set of real data samples on which the inferencing is to take place, at least first and second permutations of a set of trigger samples are generated. Every set of samples (both trigger and real samples) are then sent for homomorphic inferencing on the model at least twice, and in a secret permutated way. To improve performance, a permutation is packaged with the real data samples prior to encryption using a general purpose data structure, a tile tensor, that allows users to store multi-dimensional arrays (tensors) of arbitrary shapes and sizes. In response to receiving one or more results from the HE-based model inferencing, a determination is made whether the service provider is compromised. Upon a determination that the service provider is compromised, a given mitigation action is taken.

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 optimizing a deep learning (DL) computer model for homomorphic encryption (HE) workload processing. The mechanisms receive an original DL computer model architecture that is to be optimized for HE workload processing, and modifying the original DL computer model architecture by replacing a self-attention layer of the original DL computer model with an HE friendly self-attention layer that comprises a Power SoftMax function that does not have exponent terms, to thereby generate a modified DL computer model architecture. The mechanisms execute a machine learning training of the modified DL computer model architecture, approximate one or more elements of the Power SoftMax function with polynomials to generate a trained HE optimized DL computer model, and output the trained HE optimized DL computer model for execution on HE workloads.

Abstract: Provided are a computer program product, system, and method for processing a subset of a feature set to determine where to process a query. One of the following is performed: 1) in response to the determining to process the query at a first machine learning model, forwarding non-sensitive input data of the query to the first machine learning model to produce a first query result to return to an initiator of the query; and 2) in response to determining to process the query at a second machine learning model, forwarding sensitive input data and the non-sensitive input data to an encryption engine to encrypt to send to the second machine learning model; and receiving an encrypted second query result from the second machine learning model to decrypt to produce a second query result to return to the initiator of the query.

Abstract: 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.

Abstract: An embodiment appends, into a concatenated table, a second plurality of records in a second table to a first plurality of records in a first table. An embodiment sorts, according to each identification value in the concatenated table, the concatenated table. An embodiment generates, using an equality mask derived from each identification value in the sorted table, an intersection table, the intersection table comprising a record in the first plurality of records with a first identifier value matching a second identifier value in a record in the second plurality of records. An embodiment generates, using a not-in-intersection mask derived from the equality mask, a not-in-intersection table. An embodiment adds contents of the intersection table and contents of the not-in-intersection table together, resulting in a join table.

Abstract: An embodiment appends, into a concatenated table, a second plurality of records in a second table to a first plurality of records in a first table. An embodiment sorts, according to each identification value in the concatenated table, the concatenated table. An embodiment generates, using an equality mask derived from each identification value in the sorted table, an intersection table, the intersection table comprising a record in the first plurality of records with a first identifier value matching a second identifier value in a record in the second plurality of records. An embodiment generates, using a not-in-intersection mask derived from the equality mask, a not-in-intersection table. An embodiment adds contents of the intersection table and contents of the not-in-intersection table together, resulting in a join table.

Abstract: 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.

Abstract: A computer-implemented method for generating hash values to determine string similarity is disclosed. The computer-implemented method includes converting a first text string of a first data set into a first set of shingles. The computer-implemented method further includes determining a weight associated with each shingle in the first set of shingles based, at least in part, on a particular record field associated with a shingle. The computer-implemented method further includes generating, based on a hash function, a hash value for each shingle in the first set of shingles. The computer-implemented method further includes reducing the hash value generated for each shingle in the first set of shingles, based, at least in part on the weight associated with the shingle.

Abstract: 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.

Abstract: A method and system for evaluating and selecting an optimal packing solution (or solutions) for data that is run through a fully homomorphic encryption (FHE) simulation. In some instances, a user selected model architecture is provided in order to start simulating multiple potential configurations. Additionally, the cost of each simulated configuration is taken into account when determining an optimal packing solution.

Abstract: A computer-implemented method comprising: generating, from a key-seed associated with a user, a set of homomorphic encryption (HE) keys associated with an HE scheme; receiving, from a key management system (KMS) associated with said HE scheme, an encrypted version of said key-seed; storing said encrypted version of said key-seed, and said set of HE keys, in an untrusted storage location; and at a decryption stage, decrypting an encrypted computation result generated using said HE scheme, by: (i) recalling, from said untrusted storage location, said encrypted version of said key-seed, (ii) providing said encrypted version of said key-seed to said KMS, to obtain a decrypted version of said key-seed s associated with said user, (iii) generating, from said received decrypted version of said key-seed, a secret HE key associated with said HE scheme, and (iv) using said secret HE key to decrypt said encrypted computation result.

Abstract: A second set of data identifiers, comprising identifiers of data usable in federated model training by a second data owner, is received at a first data owner from the second data owner. An intersection set of data identifiers is determined at the first data owner. At the first data owner according to the intersection set of data identifiers, the data usable in federated model training is rearranged by the first data owner to result in a first training dataset. At the first data owner using the intersection set of data identifiers, the first training dataset, and a previous iteration of an aggregated set of model weights, a first partial set of model weights is computed. An updated aggregated set of model weights, comprising the first partial set of model weights and a second partial set of model weights from the second data owner, is received from an aggregator.

Abstract: 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.

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 train and stabilize a machine learning model using public data. The processor can fine-tune the machine learning model using anonymized private data. The processor can fine-tune the machine learning model using encrypted private data.

Abstract: A skip-connections analysis method, system, and computer program product for accelerating neural networks by removing skip-connections and efficient skip-connection realization.

Abstract: An example system can include a processor to receive a ciphertext including a fully homomorphic encrypted (FHE) time series from a client device. The processor can train an ARIMA model on the ciphertext using an estimated error and approximating polynomials. The processor can generate an encrypted report and send the encrypted report to the client device.