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A differentially private algorithm for location data release

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Abstract

The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations: private location clustering shrinks the randomized domain and cluster weight perturbation hides the weights of locations, while private location selection hides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets, GeoLife, Flickr, Div400 and Instagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

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Notes

  1. http://www.multimediaeval.org/.

  2. http://instagram.com.

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Acknowledgments

This work is supported by the Natural Science Foundation of HuBei province (China) under Grant No. 2014CFB354, Beijing Key Lab of Intelligent Telecommunication Software and Multimedia (ITSM201502) and Guangxi Key Laboratory of Trusted Software (KX201418).

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Authors and Affiliations

  1. School of Information and Security Engineering, Zhongnan University of Economics and Law, 182 Nanhu Avenue, Wuhan, 430073, China

    Ping Xiong

  2. School of Information Technology, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

    Tianqing Zhu & Gang Li

  3. Institute of Information Engineering, Chinese Academy of Sciences, 89A Minzhuang Road, Haidian District, Beijing, 100093, China

    Wenjia Niu

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  1. Ping Xiong
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  2. Tianqing Zhu
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  3. Wenjia Niu
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  4. Gang Li
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Correspondence to Wenjia Niu or Gang Li.

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Xiong, P., Zhu, T., Niu, W. et al. A differentially private algorithm for location data release. Knowl Inf Syst 47, 647–669 (2016). https://doi.org/10.1007/s10115-015-0856-1

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