Lei Zou
Lei Chen
M. Tamer Özsu
Abstract
The growing popularity of social networks has generated interesting data management and data mining problems. An important concern in the release of these data for study is their privacy, since social networks usually contain personal information. Simply removing all identifiable personal information (such as names and social security number) before releasing the data is insufficient. It is easy for an attacker to identify the target by performing different structural queries. In this paper we propose k-automorphism to protect against multiple structural attacks and develop an algorithm (called KM) that ensures k-automorphism. We also discuss an extension of KM to handle "dynamic" releases of the data. Extensive experiments show that the algorithm performs well in terms of protection it provides.
- L. Backstrom, C. Dwork, and J. M. Kleinberg. Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In WWW, 2007. Google Scholar
Digital Library
- L. Backstrom, D. P. Huttenlocher, J. M. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD, 2006. Google ScholarDigital Library
- Y. Bu, A. W.-C. Fu, R. C.-W. Wong, L. C. 0002, and J. Li. Privacy preserving serial data publishing by role composition. PVLDB, 1(1). Google ScholarDigital Library
- G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. Anonymizing bipartite graph data using safe groupings. PVLDB, 1(1). Google ScholarDigital Library
- K. B. Frikken and P. Golle. Private social network analysis: how to assemble pieces of a graph privately. In WPES, 2006. Google ScholarDigital Library
- G. Ghinita, Y. Tao, and P. Kalnis. On the anonymization of sparse high-dimensional data. In ICDE, 2008. Google ScholarDigital Library
- M. Hay, G. Miklau, D. Jensen, D. Towsley, and P. Weis. Resisting structural reidentification in anonymized social networks. In VLDB, 2008. Google ScholarDigital Library
- M. Hay, G. Miklau, D. Jensen, P. Weis, and S. Srivastava. Anonymizing social networks. Technical report, University of Massachusetts Amherst, 2007.Google Scholar
- R. J. B. Jr. and R. Agrawal. Data privacy through optimal k-anonymization. In ICDE, 2005.Google Scholar
- R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In KDD, 2006. Google ScholarDigital Library
- M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. Data Min. Knowl. Discov., 11(3), 2005. Google ScholarDigital Library
- J. Lauri and R. Scapellato. Topics in Graph Automorphisms and Reconstruction. Cambirdge University Press.Google Scholar
- K. Liu and E. Terzi. Towards identity anonymization on graphs. In SIGMOD, 2008. Google ScholarDigital Library
- A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In ICDE, 2006. Google ScholarDigital Library
- P. Samarati and L. Sweeney. Generalizing data to provide anonymity when disclosing information (abstract). In PODS, 1998. Google ScholarDigital Library
- L. Sweene. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 2002. Google ScholarDigital Library
- H. Wang, G. Yan, and Y. Xiao. Symmetry in world trade networks. Journal of Systems Science and Complexity, 2008.Google Scholar
- X. Xiao and Y. Tao. Anatomy: Simple and effective privacy preservation. In VLDB, 2006. Google ScholarDigital Library
- X. Xiao and Y. Tao. M-invariance: towards privacy preserving re-publication of dynamic datasets. In SIGMOD Conference, 2007. Google ScholarDigital Library
- Y. Xiao, M. Xiong, W. Wang, and H. Wang. Emergence of symmetry in complex networks. Physical Review E, 77(6), 2008.Google Scholar
- X. Ying and X. Wu. Randomizing social networks: a spectrum preserving approach. In SDM, 2008.Google ScholarCross Ref
- K. Zhang, J. T.-L. Wang, and D. Shasha. On the editing distance between undirected acyclic graphs and related problems. In CPM, 1995.Google ScholarCross Ref
- B. Zhou and J. Pei. Preserving privacy in social networks against neighborhood attacks. In ICDE, 2008. Google ScholarDigital Library
Index Terms
- k-automorphism: a general framework for privacy preserving network publication
Recommendations
A secure K-automorphism privacy preserving approach with high data utility in social networks
The prevalence of social networks has raised the concern for individual privacy leakage. Although preserving user privacy in social networks has been studied extensively, there is still a lot of room for improvement in the state of the art techniques. ...
Uses and gratifications of social networking sites for bridging and bonding social capital
Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
Privacy antecedents for SNS self-disclosure
Social networking sites privacy issues and self-disclosure are examined.A research model of privacy issues and self-disclosure is built.Structural equations modeling is used to assess the model fit.Path analysis is done to analyze hypothesis whereas 11 ...
Comments