Name
Abstract | ||
|---|---|---|
The popularity of location-based services leads to serious concerns on user privacy. A common mechanism to protect users' location and query privacy is spatial generalisation. As more user information becomes available with the fast growth of Internet applications, e.g., social networks, attackers have the ability to construct users' personal profiles. This gives rise to new challenges and reconsideration of the existing privacy metrics, such as k-anonymity. In this paper, we propose new metrics to measure users' query privacy taking into account user profiles. Furthermore, we design spatial generalisation algorithms to compute regions satisfying users' privacy requirements expressed in these metrics. By experimental results, our metrics and algorithms are shown to be effective and efficient for practical usage. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2133601.2133608 | CODASPY |
Keywords | Field | DocType |
account user profile,spatial generalisation,user information,query privacy,privacy requirement,spatial generalisation algorithm,existing privacy metrics,new challenge,location-based service,user privacy,new metrics,satisfiability,anonymity,social network,measurement,location based services,location based service | Internet privacy,Social network,Generalization,Computer science,Computer security,Popularity,Location-based service,User information,Anonymity,Privacy software,The Internet | Conference |
Citations | PageRank | References |
14 | 0.57 | 33 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xihui Chen | 1 | 92 | 7.22 |
| Jun Pang | 2 | 219 | 33.53 |