On the Privacy of Elementary Matrices Masking-Based Verifiable (Outsourced) Computation

Privacy-preserving Verifiable (outsourced) Computation (PVC) for face recognition is a significant research topic in the information security community. Recently, an efficient elementary matrices masking-based PVC protocol for face recognition has been published in IEEE Transactions on Dependable and Secure Computing (Ren et al. 2023). In this article, we analyze the privacy property of this protocol, and demonstrate that the output distribution of the problem generation algorithm in this protocol is not computationally indistinguishable from the uniform distribution over a matrix set, which breaks the original consequence (see Theorem 1). We introduce a formal definition of a privacy model for a PVC protocol and prove that the targeted PVC protocol does not hold privacy under this model. We then present experimental results to support our theoretical analyses.