Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2020

Abstract

In this paper, we propose a privacy-preserving outsourced calculation toolkit, Pockit, designed to allow data owners to securely outsource their data to the cloud for storage. The outsourced encrypted data can be processed by the cloud server to achieve commonly-used plaintext arithmetic operations without involving additional servers. Specifically, we design both signed and unsigned integer circuits using a fully homomorphic encryption (FHE) scheme, construct a new packing technique (hereafter referred to as integer packing), and extend the secure circuits to its packed version. This achieves significant improvements in performance compared with the original secure signed/unsigned integer circuit. The secure integer circuits can be used to construct a new data mining application, which we refer to as secure k-nearest neighbours classifier, without compromising the privacy of original data. Finally, we prove that the proposed Pockit achieves the goal of secure computation without privacy leakage to unauthorized parties, and demonstrate the utility and efficiency of Pockit.

Keywords

Privacy-preserving, outsourced computation, fully homomorphic encryption, cloud privacy

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Dependable and Secure Computing

Volume

17

Issue

5

First Page

898

Last Page

911

ISSN

1545-5971

Identifier

10.1109/TDSC.2018.2816656

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TDSC.2018.2816656

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