Paper 2019/1346

Privacy-Preserving Decentralised Singular Value Decomposition

Bowen Liu and Qiang Tang


With the proliferation of data and emerging data-driven applications, how to perform data analytical operations while respecting privacy concerns has become a very interesting research topic. With the advancement of communication and computing technologies, e.g. the FoG computing concept and its associated Edge computing technologies, it is now appealing to deploy decentralized data-driven applications. Following this trend, in this paper, we investigate privacy-preserving singular value decomposition (SVD) solutions tailored for these new computing environments. We first analyse a privacy-preserving SVD solution by Chen et al., which is based on the Paillier encryption scheme and some heuristic randomization method. We show that (1) their solution leaks statistical information to an individual player in the system; (2) their solution leaks much more information when more than one players collude. Based on the analysis, we present a new solution, which distributes the SVD results into two different players in a privacy-preserving manner. In comparison, our solution minimizes the information leakage to both individual player and colluded ones, via randomization and threshold homomorphic encryption techniques.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. MINOR revision.ICICS 2019
Internet of ThingsFog ComputingEdge ComputingSingular Value DecompositionPaillier EncryptionThreshold Cryptosystem
Contact author(s)
qiang tang @ list lu
2019-11-22: received
Short URL
Creative Commons Attribution


      author = {Bowen Liu and Qiang Tang},
      title = {Privacy-Preserving Decentralised Singular Value Decomposition},
      howpublished = {Cryptology ePrint Archive, Paper 2019/1346},
      year = {2019},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.