Case Study: User Identification for Mobile Privacy

Xiang Cheng, Luoyang Fang, Liuqing Yang, Shuguang Cui

Research output: Chapter in Book/Report/Conference proceedingChapter


To facilitate the novel mobile data-driven applications and services as discussed in previous chapters, mobile big data with spatiotemporal information may need to be released to third parties or even to the public. However, direct data publishing may lead to a significant subscriber’s privacy leakage risk (Cheng et al. IEEE Netw 31(1):72–79, 2017), immediately resulting in data availability issues. To protect subscribers’ privacy, the common practice is to anonymize the dataset by replacing subscribers’ identifiers (e.g., name, social security number, etc.) with randomly generated strings.

Original languageEnglish (US)
Title of host publicationWireless Networks(United Kingdom)
PublisherSpringer Science and Business Media B.V.
Number of pages29
StatePublished - 2018
Externally publishedYes

Publication series

NameWireless Networks(United Kingdom)
ISSN (Print)2366-1186
ISSN (Electronic)2366-1445

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.


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