Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Node-and edge-deletion NP-complete problems
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th international conference on World Wide Web
Challenges in mining social network data: processes, privacy, and paradoxes
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Link privacy in social networks
Proceedings of the 17th ACM conference on Information and knowledge management
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Eight friends are enough: social graph approximation via public listings
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
Prying Data out of a Social Network
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
| Hi-index | 0.00 |
We consider the problem of releasing a limited public view of a sensitive graph which reveals at least k edges per node. We are motivated by Facebook's public search listings, which expose user profiles to search engines along with a fixed number of each user's friends. If this public view is produced by uniform random sampling, an adversary can accurately approximate many sensitive features of the original graph, including the degree of individual nodes. We propose several schemes to produce public views which hide degree information. We demonstrate the practicality of our schemes using real data and show that it is possible to mitigate inference of degree while still providing useful public views.