Overview of Privacy Protection Data Release Anonymity Technology

Abstract The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensifified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academic and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance,and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on micro aggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the micro-aggregation anonymous method. The dynamic update program is introduced into the proposed micro-aggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.

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Epigenome data release: a participant-centered approach to privacy protection

Genome Biology ◽

10.1186/s13059-015-0723-0 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 21

Author(s):

Stephanie O. M. Dyke ◽

Warren A. Cheung ◽

Yann Joly ◽

Ole Ammerpohl ◽

Pavlo Lutsik ◽

...

Keyword(s):

Privacy Protection ◽

Data Release

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Differential privacy EV charging data release based on variable window

PeerJ Computer Science ◽

10.7717/peerj-cs.481 ◽

2021 ◽

Vol 7 ◽

pp. e481

Author(s):

Rixuan Qiu ◽

Xiong Liu ◽

Rong Huang ◽

Fuyong Zheng ◽

Liang Liang ◽

...

Keyword(s):

Privacy Protection ◽

Differential Privacy ◽

Service Providers ◽

Real Data ◽

Sampling Interval ◽

Time Data ◽

User Privacy ◽

Protection Mechanism ◽

Data Release ◽

Variable Window

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.

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