计算机科学
云计算
计算机安全
云存储
信息隐私
外包
加密
保密
同态加密
信息敏感性
作者
Ximeng Liu,Robert H. Deng,Kim-Kwang Raymond Choo,Yang Yang,HweeHwa Pang
出处
期刊:IEEE Transactions on Dependable and Secure Computing
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:17 (5): 898-911
被引量:28
标识
DOI:10.1109/tdsc.2018.2816656
摘要
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 $k$ 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI