Name
Abstract | ||
|---|---|---|
Location-Based Services (LBSs) gain increasing popularity with the development of social networks and mobile devices. The mobile users enjoy convenience by submitting their private information. Nonetheless, the users' sensitive information may be abused by an un-trusted LBS server. Privacy concerned in LBSs can be categorized into two major types: location privacy and query privacy. In this paper, we propose a novel scheme, called Encounter-Based Privacy-Preserving Scheme (EPS), which allows a user to access an LBS server under the protection of k-anonymity on both her location privacy and query privacy. Without reliance on any Trusted Third Party (TTP), EPS uses a buffer on each user's mobile device to collect the queried information of the encountered users. To achieve k-anonymity, a user needs to choose k-1 records from her buffer, with the help of our location obfuscating algorithm and querying algorithm, the user's privacy can be protected. Evaluation results show the effectiveness and efficiency of our proposed EPS. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GLOCOM.2013.6831391 | Global Communications Conference |
Keywords | Field | DocType |
data privacy,mobile computing,query processing,social networking (online),LBS,TTP,encounter based privacy preserving scheme,location based services,location privacy,mobile device,mobile devices,mobile users,private information,query privacy,querying algorithm,social networks,trusted third party,user sensitive information,Location Privacy,Location-Based Services,Query Privacy,k-anonymity | Trusted third party,Internet privacy,Next-generation network,Computer security,Computer science,Server,Computer network,Location-based service,Mobile device,Information sensitivity,Private information retrieval,Privacy software | Conference |
ISSN | Citations | PageRank |
2334-0983 | 7 | 0.45 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ben Niu | 1 | 249 | 22.20 |
| Xiaoyan Zhu | 2 | 359 | 18.33 |
| Xiaosan Lei | 3 | 11 | 1.53 |
| Weidong Zhang | 4 | 383 | 67.45 |
| Hui Li | 5 | 814 | 92.33 |