Open Access

ARTICLE

δ-Calculus: A New Approach to Quantifying Location Privacy☆

by Lihua Yin1, Ran Li, Jingquan Ding, Xiao Li, Yunchuan Guo, Huibing Zhang, Ang Li

☆This is an extended version of our conference paper [Ding, Li, Guo et al. (2018)].
1 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
2 Institute of Information Engineering, CAS, Beijing, 100093, China.
3 Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy Science, Urumqi, 830011, China.
4 Key laboratory of Speech Language Information Processing of Xinjiang, Urumqi, 830046, China.
5 University of Chinese Academy of Sciences, Beijing, 100049, China.
6 Guilin University of Electronic Technology, Guilin, 541004, China.
7 Department of Electrical and Computer Engineering, Duke University, Durham, 27708, USA.

* Corresponding Authors: Ran Li. Email: ; Jingquan Ding. Email: .

Computers, Materials & Continua 2020, 63(3), 1323-1342. https://doi.org/10.32604/cmc.2020.09667

Received 22 January 2020; Accepted 28 February 2020; Issue published 30 April 2020

Abstract

With the rapid development of mobile wireless Internet and high-precision localization devices, location-based services (LBS) bring more convenience for people over recent years. In LBS, if the original location data are directly provided, serious privacy problems raise. As a response to these problems, a large number of location-privacy protection mechanisms (LPPMs) (including formal LPPMs, FLPPMs, etc.) and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy leakage. However, existing schemes independently consider FLPPMs and evaluation metrics, without synergizing them into a unifying framework. In this paper, a unified model is proposed to synergize FLPPMs and evaluation metrics. In detail, the probabilistic process calculus (called δ-calculus) is proposed to characterize obfuscation schemes (which is a LPPM) and integrate α-entropy to δ-calculus to evaluate its privacy leakage. Further, we use two calculus moving and probabilistic choice to model nodes’ mobility and compute its probability distribution of nodes’ locations, and a renaming function to model privacy leakage. By formally defining the attacker’s ability and extending relative entropy, an evaluation algorithm is proposed to quantify the leakage of location privacy. Finally, a series of examples are designed to demonstrate the efficiency of our proposed approach.

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Citations