Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 16th international conference on World Wide Web
Discovering Structural Anomalies in Graph-Based Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
Mining (Social) Network Graphs to Detect Random Link Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Evaluating Statistical Tests for Within-Network Classifiers of Relational Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Class-based graph anonymization for social network data
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
A generalization based approach for anonymizing weighted social network graphs
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Protecting sensitive relationships against inference attacks in social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Privacy preservation of user history graph
WISTP'12 Proceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Anonymizing Subsets of Social Networks with Degree Constrained Subgraphs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
STK-anonymity: k-anonymity of social networks containing both structural and textual information
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Efficiently anonymizing social networks with reachability preservation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Sensitive and Neighborhood Privacy on Shortest Paths in the Cloud
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user's personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker's background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users' protection requirements. We verify the effectiveness of the framework through extensive experiments.