Privacy-Preserving Crowd-Sourced Statistical Data Publishing with An Untrusted Server

被引:104
作者
Wang, Zhibo [1 ]
Pang, Xiaoyi [1 ]
Chen, Yahong [1 ]
Shao, Huajie [2 ]
Wang, Qian [1 ]
Wu, Libing [3 ]
Chen, Honglong [4 ]
Qi, Hairong [5 ]
机构
[1] Wuhan Univ, Sch Cyber Sci & Engn, Key Lab Aerosp Informat Secur & Trusted Comp, Minist Educ, Wuhan 430072, Hubei, Peoples R China
[2] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA
[3] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China
[4] China Univ Petr, Coll Informat & Control Sci, Beijing 266580, Peoples R China
[5] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA
基金
中国国家自然科学基金;
关键词
Mobile crowdsensing; data publishing; untrusted server; differential privacy; privacy-preserving; LOCATION PRIVACY;
D O I
10.1109/TMC.2018.2861765
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The continuous publication of aggregate statistics over crowd-sourced data to the public has enabled many data mining applications (e.g., real-time traffic analysis). Existing systems usually rely on a trusted server to aggregate the spatio-temporal crowd-sourced data and then apply differential privacy mechanism to perturb the aggregate statistics before publishing to provide strong privacy guarantee. However, the privacy of users will be exposed once the server is hacked or cannot be trusted. In this paper, we study the problem of real-time crowd-sourced statistical data publishing with strong privacy protection under an untrusted server. We propose a novel distributed agent-based privacy-preserving framework, called DADP, that introduces a new level of multiple agents between the users and the untrusted server. Instead of directly uploading the check-in information to the untrusted server, a user can randomly select one agent and upload the check-in information to it with the anonymous connection technology. Each agent aggregates the received crowd-sourced data and perturbs the aggregated statistics locally with Laplace mechanism. The perturbed statistics from all the agents are further combined together to form the entire perturbed statistics for publication. In particular, we propose a distributed budget allocation mechanism and an agent-based dynamic grouping mechanism to realize global w-event is an element of-differential privacy in a distributed way. We prove that DADP can provide w-event is an element of-differential privacy for real-time crowd-sourced statistical data publishing under the untrusted server. Extensive experiments on real-world datasets demonstrate the effectiveness of DADP.
引用
收藏
页码:1356 / 1367
页数:12
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