Mobile Privacy: Scalable Ensemble Matching for User Identification Attacks

Luoyang Fang, Haonan Wang, Xiang Cheng, Liuqing Yang, Shuguang Cui

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Mobile privacy is broadly concerning in the mobile big data era, as user mobility behaviors are privacy-sensitive and unique. User identification attacks consist of one of the most critical privacy concerns on mobile big data. In this paper, we study mobile privacy in terms of user identifiability from the perspective of privacy adversaries. User identification in two datasets from the same data source or two different data sources is generally formulated as a linear assignment problem (LAP), in which the cost matrix of users is generated by a single distance measure. However, user identification via one single distance measure may lead to a large portion of false matches, especially when only a few users coexist across these two datasets. In addition, the cubic computational complexity of LAP limits the scale of user identification analysis. In this paper, we propose a multi-feature ensemble matching framework to improve the user identification precision based on a majority voting rule, by integrating multiple distance measures. The computational complexity of the proposed ensemble matching algorithm is an order of magnitude less than that of the single-distance based approach, which results from solving an LAP on a highly sparse matrix rather than a dense matrix. Experiments demonstrate the superior performance of our proposed scalable ensemble matching framework with respect to matching precision as well as the vulnerability of mobile network subscribers' privacy.

Original languageEnglish (US)
Article number9094666
Pages (from-to)97243-97257
Number of pages15
JournalIEEE Access
StatePublished - 2020

Bibliographical note

Funding Information:
This work was supported in part by the Key-Area Research and Development Program of Guangdong Province Project under Grant 2018B030338001, in part by the Natural Science Foundation of China under Grant NSFC-61629101, in part by the Natural Science Foundation under Grant DMS-1737795, Grant DMS-1923142, and Grant CNS-1932413, in part by the Open Research Fund from Shenzhen Research Institute of Big Data under Grant 2019ORF01006, in part by the National Key Research and Development Program of China under Grant 2018YFB1800800, and in part by the Guangdong Zhujiang Project under Grant 2017ZT07X152.

Publisher Copyright:
© 2013 IEEE.


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