Paper 2017/405

Security Analysis of ``PSLP: Privacy-Preserving Single-Layer Perceptron Learning for e-Healthcare''

Jingjing Wang, Xiaoyu Zhang, Jingjing guo, and Jianfeng Wang


With the synchronous development of both cloud computing and machine learning techniques, the clients are preferring to resort to the cloud server with substantial resources to train learning model. However, in this outsourcing paradigm it is of vital significance to address the privacy concern of client's data. Many researchers have been focusing on preserving the privacy of client's data in learning model. Recently, Wang et al. presented a privacy-preserving single-layer perceptron learning for e-healthcare scheme with using paillier cryptosystem and claimed that their scheme can protect the privacy of user's medical information. By analysing the process of iteration and the communication between the cloud and the user, we present that the honest-but-curious cloud can obtain the private medical information. Besides, the leakage of medical cases will lead to the exposure of the specific single-layer perceptron model of e-healthcare, which has gigantic commercial value.

Available format(s)
Publication info
Preprint. MAJOR revision.
Outsourcing Computation
Contact author(s)
jfwang @ xidian edu cn
2017-05-11: received
Short URL
Creative Commons Attribution


      author = {Jingjing Wang and Xiaoyu Zhang and Jingjing guo and Jianfeng Wang},
      title = {Security Analysis of ``PSLP: Privacy-Preserving Single-Layer Perceptron Learning for e-Healthcare''},
      howpublished = {Cryptology ePrint Archive, Paper 2017/405},
      year = {2017},
      note = {\url{}},
      url = {}
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