An Extended Framework of Privacy-Preserving Computation with Flexible Access Control

Research output: Contribution to journalArticle


Research units

  • Xidian University
  • Singapore Management University
  • Huazhong University of Science and Technology
  • Saint Francis Xavier University
  • Muroran Institute of Technology


Cloud computing offers various services based on outsourced data by utilizing its huge volume of resources and great computation capability. However, it also makes users lose full control over their data. To avoid the leakage of user data privacy, encrypted data are preferred to be uploaded and stored in the cloud, which unfortunately complicates data analysis and access control. In particular, few existing works consider the fine-grained access control over the computational results from ciphertexts. Though our previous work proposed a framework to support several basic computations (such as addition, multiplication and comparison Ding2017) with flexible access control, privacy-preserving division calculations over encrypted data, as a crucial operation in many statistical processes and machine learning algorithms, is neglected. In this paper, we propose four privacy-preserving division computation schemes with flexible access control to fill this gap, which can adapt to various application scenarios. Furthermore, we extend a division scheme over encrypted integers to support privacy-preserving division over multiple data types including fixed-point numbers and fractional numbers. Finally, we give their security proof and show their efficiency and superiority through comprehensive simulations and comparisons with existing work.


Original languageEnglish
Publication statusE-pub ahead of print - 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Access control, Computational modeling, Cloud computing, Protocols, Servers, Encryption, Cloud Computing, Secure Division Computation, Privacy Preservation, Access Control, Data Security.

ID: 39439765