Visible to the public Dummy Location Selection Scheme for K-Anonymity in Location Based Services

TitleDummy Location Selection Scheme for K-Anonymity in Location Based Services
Publication TypeConference Paper
Year of Publication2017
AuthorsWu, D., Zhang, Y., Liu, Y.
Conference Name2017 IEEE Trustcom/BigDataSE/ICESS
Date Publishedaug
Keywordsanonymity, artificial intelligence, cloaking area based algorithm, Complexity theory, composability, data privacy, dummy location, Geometry, Human Behavior, K-1 dummy locations, k-anonymity, LBS, localization information, location based services, low complexity dummy location selection scheme, Metrics, mobile computing, multiobjective optimization problem, optimisation, Optimization, privacy, privacy concerns, probability, pubcrawl, query probability, query processing, resilience, Resiliency, security, security analysis, security of data, Servers
Abstract

Location-Based Service (LBS) becomes increasingly important for our daily life. However, the localization information in the air is vulnerable to various attacks, which result in serious privacy concerns. To overcome this problem, we formulate a multi-objective optimization problem with considering both the query probability and the practical dummy location region. A low complexity dummy location selection scheme is proposed. We first find several candidate dummy locations with similar query probabilities. Among these selected candidates, a cloaking area based algorithm is then offered to find K - 1 dummy locations to achieve K-anonymity. The intersected area between two dummy locations is also derived to assist to determine the total cloaking area. Security analysis verifies the effectiveness of our scheme against the passive and active adversaries. Compared with other methods, simulation results show that the proposed dummy location scheme can improve the privacy level and enlarge the cloaking area simultaneously.

URLhttp://ieeexplore.ieee.org/document/8029472/
DOI10.1109/Trustcom/BigDataSE/ICESS.2017.269
Citation Keywu_dummy_2017