安全高效的隐私保护公共可验证矩阵乘法外包计算方案

doi:10.11959/j.issn.1000-0801.2023251

Abstract

Abstract:

Outsourced computation allows data owners with limited resources to delegate complex computations to resource-rich cloud servers.Matrix multiplication has important applications in scientific computing and cryptography.Verifiable outsourced computation for matrix multiplication enables data owners to outsource the matrix $M$ and the request vector $x$ to untrusted cloud servers for multiplication computation, while verifying the correctness and integrity of the computation results returned by the cloud server.However, existing solutions fail to simultaneously address the following issues: privacy of the outsourced matrix $M$ , privacy of the request vector $x$ , lack of support for public verification, and inefficiency for practical applications.To tackle these problems, proposes a secure efficient privacy-preserving publicly verifiable outsourced computation scheme for matrix multiplication was proposed, and formal definitions and security definitions for the model were provided.Matrix blinding techniques were used to preserve the privacy of the outsourced matrix $M$ and the request vector $x$ , while algebraic pseudorandom functions were employed to achieve public verification of the computation results and overall efficiency of the scheme.Theoretical and experimental results demonstrate that compared with the existing schemes, the proposed scheme not only guarantees the privacy of outsourced matrix $M$ and request vector $x$ , but also supports public verification, which offers more comprehensive functionality.At the same time, the overall computation efficiency of the proposed scheme is higher, which can improve at least 14% compared with existing schemes, and has higher practical value.

Key words: verifiable computation, privacy-preserving, public verification, cloud computing

CLC Number:

TP309

Shoudao SUN, Shen YANG, Yiheng CHEN, Qiang WANG. Secure efficient privacy-preserving publicly verifiable outsourced computation scheme for matrix multiplication[J]. Telecommunications Science, 2023, 39(12): 110-121.

Figures/Tables 10

功能	PVDC^[19]	BVC^[21]	PVDM^[15]	ESVOM^[20]	本文方案
外包可验证	√	√	√	√	√
公共验证	√	×	√	√	√
对 $M$ 的隐私性	×	×	×	×	√
对 $x$ 的隐私性	×	×	×	×	√

References 17

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算法	PVDC^[19]	BVC^[21]	PVDM^[15]	ESVOM^[20]	本文方案
KeyGen	O(mn)	O(mn)	O(mn)	O(1)	O(mn)
ProbGen	O(n)	O(m+n)	O(m+n)	O(mn)	O(m+n)
Comp	O(mn)	O(mn)	O(mn)	O(mn)	O(mn)
Verify	O(m)	O(m)	O(m)	O(m)	O(m)
Solve	—	—	—	—	O(δmn)

算法	PVDC^[19]	BVC^[21]	PVDM^[15]	ESVOM^[20]	本文方案
KeyGen	O(mn)	O(mn)	O(mn)	—	O(mn)
ProbGen	O(m+n)	O(m+n)	O(m+n)	O(m)	O(m+n)
Comp	O(m)	O(m)	O(m)	O(m)	O(m)
Verify	O(m)	O(m)	O(m)	O(m)	O(m)
Solve	—	—	—	—	—

Secure efficient privacy-preserving publicly verifiable outsourced computation scheme for matrix multiplication

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