High-Precision Privacy-Preserving Real-Valued Function Evaluation

Abstract

A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data. The multi-party computations can include a secret share reduction that transforms an instance of computed secret shared data stored in floating-point representation into an equivalent, equivalently precise, and equivalently secure instance of computed secret shared data having a reduced memory storage requirement.

Description

RELATED APPLICATIONS[0001]The subject matter of this application is related to U.S. Provisional Application No. 62 / 552,161, filed on 2017 Aug. 30, U.S. Provisional Application No. 62 / 560,175, filed on 2017 Sep. 18, U.S. Provisional Application No. 62 / 641,256, filed on 2018 Mar. 9, and U.S. Provisional Application No. 62 / 647,635, filed on 2018 Mar. 24, all of which applications are incorporated herein by reference in their entireties.BACKGROUND OF THE INVENTION[0002]There exist problems in privacy-preserving or secure multi-party computing that do not have effective solutions in the prior art. For example, suppose a number of organizations desire to collaborate in training a machine learning classifier in order to detect fraudulent activity, such as financial scams or phishing attacks. Each organization has a set of training data with examples of legitimate and fraudulent activity, but the individual organizations want to retain the privacy and secrecy of their data while still being...

AI Technical Summary

Benefits of technology

This patent describes a method for performing secure multi-party computations, which allows multiple parties to work together while keeping the input data private. The method can provide accurate results with minimal inter-party communication. It uses techniques such as Fourier series approximation and secret share reduction to achieve privacy preservation. Overall, this method allows for efficient and secure collaboration among multiple parties while maintaining privacy of input data.

Problems solved by technology

There exist problems in privacy-preserving or secure multi-party computing that do not have effective solutions in the prior art.

Existing secure multi-party computing techniques do not provide requisite levels of precision for such training without requiring unduly burdensome amounts of inter-party communication.

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