Private inference in deep neural network

A method for performing secure inference over a Deep Neural Network (DNN) using secure two-party computation, which involves a first party and a second party, to perform privacy-preserving machine learning such that a provider of the DNN does not learn anything about inputs to the DNN or about weights of the DNN beyond that which can be inferred from an output of the DNN and the inputs to the DNN, wherein said method is further performed in a manner to reduce communications between the first party and the second party via use of an oblivious transfer technique, the method comprising:

• performing a comparison that securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by the provider of the DNN, the comparison performed by:
• parsing y into q leaf strings yq-1 . . . y0, where each of the q leaf strings is more than one bit, and where y is equal to the concatenation yq-1∥ . . . ∥y0;
• compute shares of inequality 1{xn<yn} for each of at least some n from q−1 down to 1, where x is equal to the concatenation xq-1∥ . . . ∥x0, by in each case using oblivious transfer;
• compute shares of equality 1{xn=yn} for each of at least some n from q−1 down to 1 also in each case by using oblivious transfer;
• and
• recursively calculating shares of inequality of internal nodes according to the following equation:
• 1{xC<yC}=1{xB<yB}⊕1{xB=yB}∧1{xA<yA} (where xC=xB∥xA, and yC=yB∥yA), and shares of equality of internal nodes until the Boolean share of 1{x<y} is determined;
• and
• repeatedly performing the comparison for different inputs to traverse a garbled binary circuit that represents the DNN to thereby obtain a result of the garbled binary circuit.

A computing system for performing secure inference over a Deep Neural Network (DNN) using secure two-party computation, which involves a first party and a second party, to perform privacy-preserving machine learning such that a provider of the DNN does not learn anything about inputs to the DNN or about weights of the DNN beyond that which can be inferred from an output of the DNN and the inputs to the DNN, wherein said method is further performed in a manner to reduce communications between the first party and the second party via use of an oblivious transfer technique, the computing system comprising:

• one or more processing units;
• and
• computer-readable storage media having thereon instructions that are executable by the one or more processing units to cause the computing system to perform a comparison that securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by the provider of the DNN, the comparison performed by:
• parsing y into q leaf strings yq-1 . . . y0, where each of the q leaf strings is more than one bit, and where y is equal to the concatenation yq-1∥ . . . ∥y0;
• compute shares of inequality 1{xn<yn} for each of at least some n from q−1 down to 1, where x is equal to the concatenation xq-1∥ . . . ∥x0, by in each case using oblivious transfer;
• compute shares of equality 1{xn=yn} for each of at least some n from q−1 down to 1 also in each case by using oblivious transfer;
• and
• recursively calculating shares of inequality of internal nodes according to the following equation:
• 1{xC<yC}=1{xB<yB}⊕1{xB=yB}∧1{xA<yA} (where xC=xB∥xA, and yC=yB∥yA), and shares of equality of internal nodes until the Boolean share of 1{x<y} is determined;
• and
• repeatedly performing the comparison for different inputs to traverse a garbled binary circuit that represents the DNN to thereby obtain a result of the garbled binary circuit.