Privacy-preserving outsourced calculation toolkit in the cloud

• Main Authors: LIU, Ximeng, DENG, Robert H., CHOO, Kim-Kwang Raymond, YANG, Yang, PANG, Hwee Hwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Privacy-preserving; outsourced computation; fully homomorphic encryption; cloud privacy; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5302; https://ink.library.smu.edu.sg/context/sis_research/article/6305/viewcontent/Privacy_Preserving_2020_av.pdf
• Institution: Singapore Management University

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.