Protecting data privacy in publicly verifiable delegation of matrix and polynomial functions

Abstract

Outsourcing computation has gained significant attention in recent years in particular due to the prevalence of cloud computing. There are two main security concerns in outsourcing computation: guaranteeing that the server performs the computation correctly, and protecting the privacy of the client’s data. The verifiable computation of Gennaro, Gentry and Parno addresses both concerns for outsourcing the computation of a function f on an input x to the cloud. The GGP scheme is privately delegatable, privately verifiable, and based on the expensive cryptographic primitives such as fully homomorphic encryption (FHE). In this paper we consider the problem of outsourcing matrix-vector multiplications of the form \(F{\mathbf{x}}\), where F is a matrix and \(\mathbf{x}\) is a column vector, and construct publicly delegatable and publicly verifiable schemes. Our schemes are either input private or function private, highly efficient, and provably secure under the well-established assumptions such as the discrete-logarithm assumption. We decompose a polynomial computation, such as computing a univariate polynomial of arbitrary degree, a bivariate polynomial of arbitrary degree, a quadratic multivariate polynomial, and in general any multivariate polynomial, into a two-step computation in which the computaionally expensive step is a matrix-vector multiplication. We use the matrix schemes to outsource the computation of high-degree polynomials and obtain the first high-degree polynomial outsourcing schemes that simultaneously have public delegation, public verification and input privacy/function privacy.

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    http://flintlib.org/.

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    https://github.com/relic-toolkit.

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Acknowledgements

The authors would like to thank the anonymous referees for the helpful comments. The research of Liang Feng Zhang was supported by the NSFC (No. 61602304) and the Pujiang Talent Program (No. 16PJ1406500). The research of Reihaneh Safavi-Naini was in part supported by the National Science and Engineering Research Council of Canada, and Alberta Innovates Technology Futures in the province of Alberta, Canada.

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Zhang, L.F., Safavi-Naini, R. Protecting data privacy in publicly verifiable delegation of matrix and polynomial functions. Des. Codes Cryptogr. 88, 677–709 (2020). https://doi.org/10.1007/s10623-019-00704-y

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Keywords

  • Verifiable computation
  • Public delegation
  • Public verification
  • Data privacy

Mathematics Subject Classification

  • 11T71