摘要
对满足本地差分隐私的高维数值型数据收集问题进行了研究。设计了一种基于随机投影技术的满足本地差分隐私的高维数值型数据收集算法Multi-RPHM,在满足本地差分隐私的条件下,该算法处理维度较高的数据时能够保证所收集的数据的高效用。从理论上证明了该算法满足ε-本地差分隐私的要求。在合成数据集上进行的实验结果验证了该算法的有效性。
The problem of high-dimensional data collection satisfying local differential privacy was studied.A new locally differentially private algorithm called Multi-RPHM was proposed based on the random projection technology,which achieved the high utility of the collected high-dimensional numeric data while satisfying the local differential privacy.The algorithm was formally proved to meetε-local differential privacy.The effectiveness of the algorithm was comfirmed through experiments on synthetic datasets.
作者
孙慧中
杨健宇
程祥
苏森
SUN Huizhong;YANG Jianyu;CHENG Xiang;SU Sen(State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《大数据》
2020年第1期3-11,共9页
Big Data Research
基金
国家自然科学基金资助项目(No.61872045).
关键词
高维数值型数据
隐私保护
本地差分隐私
随机投影
high-dimensional numeric data
privacy protection
local differential privacy
random projection
