Abstract
With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.
Highlights
With the development of Information Technology, people have been enjoying more convenient life and higher quality services. is is especially prominent in the era of big data
We only focus on the survey study on Local differential privacy (LDP) privacy; we believe present common utility metrics under LDP to analyze the method of LDP
We further introduce some special problems of distribution estimation under LDP, which include small sample problem and linear queries estimation for distribution estimation
Summary
With the development of Information Technology, people have been enjoying more convenient life and higher quality services. is is especially prominent in the era of big data. Privacy issues have been becoming a hot topic in public in recent years and may become a major obstacle to the long-term development of information technology and will influence the public’s acceptance of technologies. To avoid this curse, privacy-preserving individual data have become a top priority for governments and organizations in the world. E EU passed the General Data Protection Regulation (GDPR) (https://gdprinfo.eu/) in 2016. It is insufficient for privacy preservation to only rely on these laws and regulations and it requires the support of privacy protection techniques. Compared to the anonymous-based privacy preservation methods (e.g., kanonymity [2], l-diversity [3], and t-closeness [4]) that require assumptions on some specific attack and background knowledge, differential privacy has been becoming a hotpot in academic and industry fields
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