A Linear Distinguisher and Its Application for Analyzing Privacy-Preserving Transformation Used in Verifiable (Outsourced) Computation
Title | A Linear Distinguisher and Its Application for Analyzing Privacy-Preserving Transformation Used in Verifiable (Outsourced) Computation |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Zhao, Liang, Chen, Liqun |
Conference Name | Proceedings of the 2018 on Asia Conference on Computer and Communications Security |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-5576-6 |
Keywords | ciphertext-only attack, control theory, Cyber physical system, cyber physical systems, Human Behavior, indistinguishability, privacy, privacy analysis, privacy-preserving verifiable (outsourced) computation, pubcrawl, resilience, Resiliency, Scalability |
Abstract | A distinguisher is employed by an adversary to explore the privacy property of a cryptographic primitive. If a cryptographic primitive is said to be private, there is no distinguisher algorithm that can be used by an adversary to distinguish the encodings generated by this primitive with non-negligible advantage. Recently, two privacy-preserving matrix transformations first proposed by Salinas et al. have been widely used to achieve the matrix-related verifiable (outsourced) computation in data protection. Salinas et al. proved that these transformations are private (in terms of indistinguishability). In this paper, we first propose the concept of a linear distinguisher and two constructions of the linear distinguisher algorithms. Then, we take those two matrix transformations (including Salinas et al.\$'\$s original work and Yu et al.\$'\$s modification) as example targets and analyze their privacy property when our linear distinguisher algorithms are employed by the adversaries. The results show that those transformations are not private even against passive eavesdropping. |
URL | https://dl.acm.org/citation.cfm?doid=3196494.3196505 |
DOI | 10.1145/3196494.3196505 |
Citation Key | zhao_linear_2018 |