Abstract: A non-interactive protocol is provided for evaluating machine learning models such as decision trees. A client can delegate the evaluation of a machine learning model such as a decision tree to a server by sending an encrypted input and receiving only the encryption of the result. The inputs can be encoded as vector of integers using their binary representation. The server can then evaluate the machine learning model using a homomorphic arithmetic circuit. The homomorphic arithmetic circuit provides an implementation that requires fewer multiplications than a Boolean comparison circuit. Efficient data representations are then combined with different algorithmic optimizations to keep the computational overhead and the communication cost low. Related apparatus, systems, techniques and articles are also described.

Abstract: A garbled circuit and two garbled inputs are received by a server from each pair of a plurality of clients. The garbled circuit encodes a comparison function and the garbled inputs encode a respective data value from each of the clients in each pair. Thereafter, the server evaluates the garbled circuits using the corresponding garbled inputs to result in a plurality of comparison bits. The server can then sort the datasets in an ascending or descending order by using the comparison bits to compute the rank of each data value. Using the sorted datasets, the server determines a median value for the datasets and transmits data characterizing the median value to each of the clients.

Abstract: A server receives a corresponding data value encrypted using a common threshold public key from each of a plurality of clients. The server distributes the received data values to the clients for evaluating comparison of values. The server receives the encrypted comparison results from each of the clients in response to the distribution of the received encrypted data values. The comparison results are encrypted using the common key. The server homomorphically determines a ciphertext encrypting the rank of each client's data value using the comparison results. Further, the server can compute a ciphertext encrypting the median of the datasets. Thereafter, the server can initiate a threshold decryption to generate a final result.

Abstract: A non-interactive protocol is provided for evaluating machine learning models such as decision trees. A client can delegate the evaluation of a machine learning model such as a decision tree to a server by sending an encrypted input and receiving only the encryption of the result. The inputs can be encoded as vector of integers using their binary representation. The server can then evaluate the machine learning model using a homomorphic arithmetic circuit. The homomorphic arithmetic circuit provides an implementation that requires fewer multiplications than a Boolean comparison circuit. Efficient data representations are then combined with different algorithmic optimizations to keep the computational overhead and the communication cost low. Related apparatus, systems, techniques and articles are also described.

Abstract: A non-interactive protocol is provided for evaluating machine learning models such as decision trees. A client can delegate the evaluation of a machine learning model such as a decision tree to a server by sending an encrypted input and receiving only the encryption of the result. The inputs can be encoded using their binary representation. Efficient data representations are then combined with different algorithmic optimizations to keep the computational overhead and the communication cost low. Related apparatus, systems, techniques and articles are also described.

Abstract: A garbled circuit and two garbled inputs are received by a server from each pair of a plurality of clients. The garbled circuit encodes a comparison function and the garbled inputs encode a respective data value from each of the clients in each pair. Thereafter, the server evaluates the garbled circuits using the corresponding garbled inputs to result in a plurality of comparison bits. The server can then sort the datasets in an ascending or descending order by using the comparison bits to compute the rank of each data value. Using the sorted datasets, the server determines a median value for the datasets and transmits data characterizing the median value to each of the clients.

Abstract: A server receives a corresponding data value encrypted using a common threshold public key from each of a plurality of clients. The server distributes the received data values to the clients for evaluating comparison of values. The server receives the encrypted comparison results from each of the clients in response to the distribution of the received encrypted data values. The comparison results are encrypted using the common key. The server homomorphically determines a ciphertext encrypting the rank of each client's data value using the comparison results. Further, the server can compute a ciphertext encrypting the median of the datasets. Thereafter, the server can initiate a threshold decryption to generate a final result.

Abstract: Methods, systems, and articles of manufacture are provided for oblivious order preserving encryption. A method may include: traversing, by a cloud service provider, an order preserving encryption (OPE) tree based on a result of an oblivious comparison performed by a data owner and a data client, the OPE tree having nodes that each correspond to a ciphertext of data associated with the data owner, the ciphertext of the data being stored at the cloud service provider, and a relative position of the nodes within the OPE tree corresponding to an order that is present in the data associated with the data owner; and determining, based on the traversing of the OPE tree, an OPE encoding for an input value from the data client, the OPE encoding for the input value indicative of a position of a node corresponding to the input value within the OPE tree.

Abstract: Methods, systems, and articles of manufacture are provided for oblivious order preserving encryption. A method may include: traversing, by a cloud service provider, an order preserving encryption (OPE) tree based on a result of an oblivious comparison performed by a data owner and a data client, the OPE tree having nodes that each correspond to a ciphertext of data associated with the data owner, the ciphertext of the data being stored at the cloud service provider, and a relative position of the nodes within the OPE tree corresponding to an order that is present in the data associated with the data owner; and determining, based on the traversing of the OPE tree, an OPE encoding for an input value from the data client, the OPE encoding for the input value indicative of a position of a node corresponding to the input value within the OPE tree.