计算机科学
云计算
带着错误学习
加密
钥匙(锁)
密码学
乘法(音乐)
理论计算机科学
同态加密
方案(数学)
计算机安全
信息隐私
数据挖掘
数学
数学分析
组合数学
操作系统
作者
Jun Zhang,Zoe L. Jiang,Ping Li,Siu‐Ming Yiu
标识
DOI:10.1016/j.ins.2021.06.017
摘要
Preparing large amounts of training data is the key to the success of machine learning. Due to the public’s concern about individual privacy, different techniques are proposed to achieve privacy preserving machine learning. Homomorphic encryption enables calculation on encrypted data in the cloud. However, current schemes either focus on single key or a specific algorithm. Cooperation between different institutions is quite common in this era of big data. Encrypting data from different institutions under one single key is a risk to data privacy. Moreover, constructing secure scheme for a specific machine learning algorithm lacks universality. Based on an additively homomorphic encryption supporting one multiplication, we propose a general multikey computing framework to execute common arithmetic operations on encrypted data such as addition, multiplication, comparison, sorting, division and etc. Our scheme can be used to run different machine learning algorithms. Our scheme is proven to be secure against semi-honest attackers and the experimental evaluations demonstrate the practicality of our computing framework.
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