Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2020

Abstract

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) 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.

Keywords

Access control, Computational modeling, Cloud computing, Protocols, Servers, Encryption, Cloud computing, secure division computation, privacy preservation, data security

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Network and Service Management

Volume

17

Issue

2

First Page

918

Last Page

930

ISSN

1932-4537

Identifier

10.1109/TNSM.2019.2952462

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TNSM.2019.2952462

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