Abstract: Provided are methods and systems for performing a secure machine learning analysis over an instance of data. An example method includes acquiring, by a client, a homomorphic encryption scheme, and at least one machine learning model data structure. The method further includes generating, using the encryption scheme, at least one homomorphically encrypted data structure, and sending the encrypted data structure to at least one server. The method includes executing a machine learning model, by the at least one server based on the encrypted data structure to obtain an encrypted result. The method further includes sending, by the server, the encrypted result to the client where the encrypted result is decrypted. The machine learning model includes neural networks and decision trees.

Abstract: Provided are methods and systems for performing secure analytics using term generations and a homomorphic encryption. An example method includes receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, wherein the list of indices is generated using the term generation function and the hash function, applying, by the server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices, extracting, by the server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the encrypted data set to obtain an encrypted result, and sending the encrypted result to the client to decrypt the encrypted result using a private key of the homomorphic encryption scheme.

Abstract: Methods and systems for performing a computational operation on a server host are provided. Exemplary methods include: receiving an encrypted service request from a client host, the client host encrypting a service request to produce the encrypted service request using a shared secret, the service request specifying the computational operation; decrypting, in a secure enclave, the encrypted service request using the shared secret to produce a decrypted service request, the secure enclave preventing other software running on the server host from accessing the shared secret and other data stored in a memory space; performing the computational operation, in the secure enclave, using the decrypted service request to generate a service result; encrypting, in the secure enclave, the service result using the shared secret to create an encrypted service result; and providing the encrypted service result to the client host, the client host decrypting the encrypted service result.

Abstract: Example selector derived encryption methods and systems include creating a hashed and encrypted database, as well as performing a query against the hashed and encrypted database using an encrypted selector exchange protocol to prevent the exposure of extraneous data from the hashed and encrypted database.

Abstract: Secure analytics using homomorphic and injective format-preserving encryption are disclosed herein. An example method includes encoding an analytic parameter set using a homomorphic encryption scheme as a set of homomorphic analytic vectors; transmitting the set of homomorphic analytic vectors to a server system; and receiving a homomorphic encrypted result from the server system, the server system having utilized the homomorphic encryption scheme and a first injective, format-preserving encryption scheme to evaluate the set of homomorphic analytic vectors over a datasource.

Abstract: Provided are methods and systems for query processing with adaptive risk decisioning. An example method includes receiving a query by a client in communication with plurality of servers. The method further includes analyzing, by the client, the query to select at least one server being configured to provide data of a data source, the data being associated with a portion of the query. The method includes acquiring, by the client, a security profile of the data source. The method includes generating, by the client and based on the query, at least one subquery for the server. The method includes sending, by the client, the subquery to the server. The server processes, based on the security profile, the subquery over the data, to obtain a result of the subquery. The method includes generating, by the client and based on the result of the subquery, a result for the query.

Abstract: Systems and methods of secure analytics using an encrypted analytics matrix are disclosed herein. An example method includes encoding an analytic parameter set using a homomorphic encryption scheme as a homomorphic analytic matrix, wherein the homomorphic analytic matrix is generated by extracting a set of term components from an analytic and the analytic parameter set using a term generator function; transmitting a processing set to a server system, the processing set including at least the homomorphic analytic matrix and a keyed hashing function; and receiving a homomorphic encrypted result from the server system, the server system having utilized the homomorphic encryption scheme and the keyed hashing function to evaluate the homomorphic analytic matrix over a datasource.

Abstract: Provided are methods and systems for performing a secure probabilistic analytic using an encrypted analytics matrix. An example method includes acquiring, by a client, an analytic, analytic parameters associated with the analytic, and a homomorphic encryption scheme including a public key for encryption and a private key for decryption. The method further includes generating, using the encryption scheme, an analytic matrix based on the analytic and analytic parameters, and sending the analytic matrix and the encryption scheme to at least one server. The method includes generating, by the server and based on the encryption scheme, set of terms from a data set, evaluating the analytic matrix over the set of terms to obtain an encrypted result, and estimating a probabilistic error of the encrypted result. The method further includes sending, by the server, the encrypted result and the probabilistic error to the client where the encrypted result is decrypted.

Abstract: Systems and methods for end-to-end encryption and compression are described herein. A query is encrypted at a client using a homomorphic encryption scheme. The encrypted query is sent to a server where the encrypted query is evaluated over target data to generate encrypted response without decrypting the encrypted query. The result elements of the encrypted response are grouped, co-located, and compressed, without decrypting the encrypted query or the encrypted response. The compressed encrypted response is sent to the client where it is decrypted and decompressed to obtain the results of the query without revealing the query or results to the owner of the target data, an observer, or an attacker.

Abstract: Systems and methods for an end-to-end secure operation from an expression in natural language. Exemplary methods include: receiving a set of queries from a natural language processor, the set of queries being produced by a method including: getting data schemas associated with a target data source; obtaining the expression in natural language; performing natural language processing on the expression to determine a desired operation; and generating the set of queries using at least one of matching and inference techniques over the desired operation with respect to the data schemas; encrypting the set of queries using a homomorphic encryption technique; providing the encrypted set of queries to a server, the server including the target data source; acquiring encrypted results, the encrypted results being responsive to the encrypted set of queries; and decrypting the encrypted results using a decryption key to produce desired results.

Abstract: Provided are methods and systems for performing a secure machine learning analysis over an instance of data. An example method includes acquiring, by a client, an homomorphic encryption scheme, and at least one machine learning model data structure. The method further includes generating, using the encryption scheme, at least one homomorphically encrypted data structure, and sending the encrypted data structure to at least one server. The method includes executing a machine learning model, by the at least one server based on the encrypted data structure to obtain an encrypted result. The method further includes sending, by the server, the encrypted result to the client where the encrypted result is decrypted. The machine learning model includes neural networks and decision trees.

Abstract: Systems and methods for end-to-end encryption of a web browsing process are described herein. A web query is encrypted at a client using a homomorphic encryption scheme. The encrypted query is sent to a server where the encrypted query is evaluated over web content to generate an encrypted response without decrypting the encrypted query and without decrypting the response. The encrypted response is sent to the client where it is decrypted to obtain the results of the query without revealing the query or results to the owner of the web content, an observer, or an attacker.

Abstract: Methods and systems for performing a computational operation on a server host are provided. Exemplary methods include: receiving an encrypted service request from a client host, the client host encrypting a service request to produce the encrypted service request using a shared secret, the service request specifying the computational operation; decrypting, in a secure enclave, the encrypted service request using the shared secret to produce a decrypted service request, the secure enclave preventing other software running on the server host from accessing the shared secret and other data stored in a memory space; performing the computational operation, in the secure enclave, using the decrypted service request to generate a service result; encrypting, in the secure enclave, the service result using the shared secret to create an encrypted service result; and providing the encrypted service result to the client host, the client host decrypting the encrypted service result.

Abstract: Systems and methods for an end-to-end secure operation using a query matrix Q_M having dimensions d×s. Exemplary methods include: extracting a set of term components {T} of the operation using a term generation function; partitioning a range of a keyed hash function H(T) into a set of vectors {c_T}; setting Q_M (j,m)=E(B_j,m) when c_T[j]=m for j=0, . . . , (d?1) and for m=0, . . . , (s?1), E(B_j,m) being a non-zero bitmask corresponding to element T from the set of term components {T} encrypted using a homomorphic encryption scheme E; and setting Q_M (j,m)=E(0) when c_T[j]?m for j=0, . . . , (d?1) and for m=0, . . . , (s?1), E(0) being a zero bitmask encrypted using the homomorphic encryption scheme E.

Abstract: Secure analytics using homomorphic and injective format-preserving encryption are disclosed herein. An example method includes encoding an analytic parameter set using a homomorphic encryption scheme as a set of homomorphic analytic vectors; transmitting the set of homomorphic analytic vectors to a server system; and receiving a homomorphic encrypted result from the server system, the server system having utilized the homomorphic encryption scheme and a first injective, format-preserving encryption scheme to evaluate the set of homomorphic analytic vectors over a datasource.

Abstract: Systems and methods that provide secure analytics using homomorphic and injective format-preserving encryption and an encrypted analytics matrix are disclosed herein. An example method includes encoding an analytic parameter set using a homomorphic encryption scheme as a homomorphic analytic matrix; transmitting a processing set to a server system, the processing set including at least the homomorphic analytic matrix and a keyed hashing function; and receiving a homomorphic encrypted result from the server system, the server system having utilized the homomorphic encryption scheme, the keyed hashing function, and a format preserving encryption scheme to evaluate the homomorphic analytic matrix over a datasource.

Abstract: A hardware implementations of Montgomery modular multiplication are described. The number of components as well as the number of cycles may be reduced by using a lookup table and multiplexer for selecting terms to be added during calculations. Also a loop unrolling technique may be used improve performance. A chain of pipeline adder modules and a chain of delay and shift modules may be used to pipeline calculations of multiple sets of operands.

Abstract: Provided are methods and systems for performing a secure probabilistic analytic using an encrypted analytics matrix. An example method includes acquiring, by a client, an analytic, analytic parameters associated with the analytic, and a homomorphic encryption scheme including a public key for encryption and a private key for decryption. The method further includes generating, using the encryption scheme, an analytic matrix based on the analytic and analytic parameters, and sending the analytic matrix and the encryption scheme to at least one server. The method includes generating, by the server and based on the encryption scheme, set of terms from a data set, evaluating the analytic matrix over the set of terms to obtain an encrypted result, and estimating a probabilistic error of the encrypted result. The method further includes sending, by the server, the encrypted result and the probabilistic error to the client where the encrypted result is decrypted.

Abstract: Systems and methods of secure analytics using an encrypted analytics matrix are disclosed herein. An example method includes encoding an analytic parameter set using a homomorphic encryption scheme as a homomorphic analytic matrix; transmitting a processing set to a server system, the processing set including at least the homomorphic analytic matrix and a keyed hashing function; and receiving a homomorphic encrypted result from the server system, the server system having utilized the homomorphic encryption scheme and the keyed hashing function to evaluate the homomorphic analytic matrix over a datasource.

Abstract: An example method for performing a secure probabilistic analytic includes acquiring, by a client, an analytic, at least one analytic parameter associated with the analytic, and an encryption scheme. The encryption scheme can include a public key for encryption and a private key for decryption. The method further includes generating, using the encryption scheme, at least one analytical vector based on the analytic and analytic parameter, and sending the analytical vector and the encryption scheme to at least one server. The method includes generating, by the server based on the encryption scheme, a set of terms from a data set, evaluating the analytical vector over the set of terms to obtain an encrypted result; estimating, by the server, a probabilistic error of the encrypted result; and sending, by the server, the encrypted result and the probabilistic error to the client where the encrypted result is decrypted.