Paper Info
Title
Achieving Personalized k-Anonymity against Long-Term Observation in Location-Based Services
Abstract
Location privacy continues to attract significant attentions from both industry and academia in recent years. However, Location Based Service (LBS) servers or some other adversaries who can monitor a particular user's historical and current status in a long-term way may likely infer user's location privacy. To solve this problem, we propose a Longterm Observation-aware Dummy Selection (LODS) algorithm to achieve k-anonymity for users in LBSs. Different from existing approaches, the LODS takes the historical anonymity sets into account, since mobile users may query LBSs at certain places such as home or office. LODS selects candidate sets containing dummy locations with less number of occurrences firstly, in order to achieve the preferred distribution. Then, LODS further filters out candidate sets with smaller entropy. Finally, we choose the anonymity set with highest Quality of Service (QoS) as the result. Extensive experiment indicates our algorithm can protect user's location privacy effectively against long-term observation, and satisfy user's QoS requirement at the same time.
Year
DOI
Venue
2018
10.1109/GLOCOM.2018.8647719
2018 IEEE Global Communications Conference (GLOBECOM)
Field
DocType
ISSN
Computer science,Server,Computer network,Location-based service,Quality of service,k-anonymity,Anonymity,Pound (mass)
Conference
2334-0983
ISBN
Citations 
PageRank 
978-1-5386-4727-1
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Fenghua Li126334.70
Yahong Chen232.41
Ben Niu324922.20
Yuanyuan He4216.11
Kui Geng522.07
Jin Cao6133.22