@Article{cmc.2020.09667,
AUTHOR = {Lihua Yin1, Ran Li, Jingquan Ding, Xiao Li, Yunchuan Guo, Huibing Zhang, Ang Li},
TITLE = {δ-Calculus: A New Approach to Quantifying Location Privacy☆},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1323--1342},
URL = {http://www.techscience.com/cmc/v63n3/38878},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09667}
}