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Analyzing Private Network Data Using Output Perturbation

Dan Suciu | Don Towsley | David Jensen | David D. Jensen | Vibhor Rastogi | Michael Hay | D. Towsley | Michael Hay | Vibhor Rastogi | Dan Suciu

[1] Balachander Krishnamurthy,et al. Class-based graph anonymization for social network data , 2009, Proc. VLDB Endow..

[2] Dan Suciu,et al. Relationship privacy: output perturbation for queries with joins , 2009, PODS.

[3] Sofya Raskhodnikova,et al. Smooth sensitivity and sampling in private data analysis , 2007, STOC '07.

[4] Xiaodong Wang,et al. Preserving privacy in social networks against subgraph attacks , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[5] Lei Zou,et al. K-Automorphism: A General Framework For Privacy Preserving Network Publication , 2009, Proc. VLDB Endow..

[6] Dan Suciu,et al. The Boundary Between Privacy and Utility in Data Publishing , 2007, VLDB.

[7] Alina Campan,et al. A Clustering Approach for Data and Structural Anonymity in Social Networks , 2008 .

[8] Dan Suciu,et al. Boosting the Accuracy of Differentially-Private Queries Through Consistency , 2009, ArXiv.

[9] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[10] Xiaowei Ying,et al. Randomizing Social Networks: a Spectrum Preserving Approach , 2008, SDM.