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Privacy-Preserving Publishing of Multilevel Utility-Controlled Graph Datasets

Published:22 February 2018Publication History
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Abstract

Conventional private data publication schemes are targeted at publication of sensitive datasets either after the k-anonymization process or through differential privacy constraints. Typically these schemes are designed with the objective of retaining as much utility as possible for the aggregate queries while ensuring the privacy of the individual records. Such an approach, though suitable for publishing aggregate information as public datasets, is inapplicable when users have different levels of access to the same data. We argue that existing schemes either result in increased disclosure of private information or lead to reduced utility when some users have more access privileges than the others. In this article, we present an anonymization framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privilege levels. We design and implement our proposed multilevel utility-controlled anonymization schemes in the context of large association graphs considering three levels of user utility, namely, (1) users having access to only the graph structure, (2) users having access to the graph structure and aggregate query results, and (3) users having access to the graph structure, aggregate query results, and individual associations. Our experiments on real large association graphs show that the proposed techniques are effective and scalable and yield the required level of privacy and utility for each user privacy and access privilege level.

References

  1. C. Aggarwal. 2005. On k-anonymity and the curse of dimensionality. In International Conference on Very Large Databases (VLDB’5). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. L. Backstrom, C. Dwork, and J. Kleinberg. 2007. Wherefore are thou R3579X? Anonymized social networks, hiddern patterns and structural steganography. In International Worldwide Web Conference (WWW’07). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. 2009. Class-based graph anonymization for social network data. In International Conference on Very Large Databases (VLDB’09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. Chen. 2011. Publishing set-valued data via differential privacy. In International Conference on Very Large Databases (VLDB’11).Google ScholarGoogle Scholar
  5. G. Cormode, D. Srivastava, N. Li, and T. Li. 2010. Minimizing and maximizing utility: Analyzing method-based attacks on anonymized data. In International Conference on Very Large Databases (VLDB’10). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. 2008. Anonymizing bipartite graph data using safe groupings. In International Conference on Very Large Databases (VLDB’08). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. A. Fisher and F. Yates. 1938. Statistical tables for biological, agricultural, and medical research. Oliver and Boyd, London, 20, Example 12.Google ScholarGoogle Scholar
  8. A. Friedman and A. Schuster. 2010. Data mining with differential privacy. In International Conference on Knowledge Discovery and Data Mining (SIGKDD’10). Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Samarati. 2001. Protecting respondents identities in microdata release. In Transactions on Knowledge and Data Engineering (TKDE’01). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. L. Sweeney. 2002. k-Anonymity: A model for protecting privacy. In International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. G. Ghinita, Y. Tao, and P. Kalnis. 2008. On the anonymization of sparse high-dimensional data. In International Conference on Data Engineering (ICDE’08). Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Korolova, R. Motwani, S. Nabar, and Y. Xu. 2008. Link privacy in social networks. In International Conference on Data Engineering (ICDE’08). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. 2006. l-Diversity: Privacy beyond k-anonymity. In International Conference on Data Engineering (ICDE’06). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. V. Karwa, S. Raskhodnikova, A. Smith, and G. Yaroslavtsev. 2001. Private analysis of graph structure. In International Conference on Very Large Databases (VLDB’01).Google ScholarGoogle Scholar
  15. S. Kasiviswanathan, K. Nissim, S. Raskhodnikova, and A. Smith. 2013. Analyzing graphs with node differential privacy. In Theory of Cryptography (TCC’13). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. LeFevre, D. DeWitt, and R. Ramakrishnan. 2005. Incognito: Efficient full-domain K-anonymity. In Special Interest Group on Management of Data (SIGMOD’05). Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. N. Li, T. Li, and S. Venkatasubramanian. 2007. t-Closeness: Privacy beyond k- anonymity and l-diversity. In International Conference on Data Engineering (ICDE’05).Google ScholarGoogle Scholar
  18. A. Sala, X. Zhao, C. Wilson, H. Zheng, and B. Y. Zhao. 2011. Sharing graphs using differentially private graph models. In Internet Measurement Conference (IMC’11). Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Serjantov and G. Danezis. 2002. Towards an information theoretic metric for anonymity. In Privacy Enhancing Technologies Symposium (PETS’02). Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. Task and C. Clifton. 2013. What should we protect? Defining differential privacy for social network analysis. In Social Network Analysis and Mining.Google ScholarGoogle Scholar
  21. G. Toth, Z. Hornak, and F. Vajda. 2004. Measuring anonymity revisited. In Nordic Workshop on Secure IT Systems (Nordsec).Google ScholarGoogle Scholar
  22. R. C. Wong, A. W. Fu, K. Wang, and J. Pei. 2007. Attack in privacy preserving data publishing. In International Conference on Very Large Databases (VLDB’07). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. R. Wong, J. Li, A. Fu, and K. Wang. 2006. (α, k)-Anonymity: An enhanced k-anonymity model for privacy-preserving data publishing. In International Conference on Knowledge Discovery and Data Mining (SIGKDD’06). Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C. Dwork. 2006. Differential privacy. In International Colloquium on Automata, Languages, and Programming (ICALP’06). Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. X. Xiao and Y. Tao. 2006. Anatomy: Simple and effective privacy preservation. In International Conference on Very Large Databases (VLDB’06). Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Y. Yang, Z. Zhang, G. Miklau, M. Winslett, and X. Xiao et al. 2012. Differential privacy in data publication and analysis. In Special Interest Group on Management of Data (SIGMOD’12). Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Q. Zhang, N. Koudas, D. Srivastava, and T. Yu. 2007. Aggregate query answering on anonymized tables. In International Conference on Very Large Databases (VLDB’07).Google ScholarGoogle Scholar
  28. B. Zhou and J. Pei. 2008. Preserving privacy in social networks against neighborhood attacks. In International Conference on Data Engineering (ICDE’08). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. W. Day, Ni. Li, and M. Lyu. 2016. Publishing graph degree distribution with node differential privacy. In Special Interest Group on Management of Data (SIGMOD’16). Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. J. Zhang, G. Cormode, C. Procopiuc, D. Srivastava, and X. Xiao. 2015. Private release of graph statistics using ladder functions. In Special Interest Group on Management of Data (SIGMOD’15). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. J. Blocki, A. Blum, A. Datta, and O. Sheffet. 2013. Differentially private data analysis of social networks via restricted sensitivity. In Innovations in Theoretical Computer Science (ITCS’13). Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. S. Chen and S. Zhou. 2013. Recursive mechanism: Towards node differential privacy and unrestricted joins. In Special Interest Group on Management of Data (SIGMOD’13). Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. E. Barker, M. Smid, D. Branstad, and S. Chokhani. 2013. NIST Special Publication 800 -130: A framework for designing cryptographic key management systems. In National Institute of Standards and Technology Report.Google ScholarGoogle Scholar
  34. D. Turner. 2016. What is key management? A CISO perspective. In Cryptomathic.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 18, Issue 2
      Special Issue on Internetware and Devops and Regular Papers
      May 2018
      294 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3182619
      • Editor:
      • Munindar P. Singh
      Issue’s Table of Contents

      Copyright © 2018 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 February 2018
      • Accepted: 1 July 2017
      • Revised: 1 May 2017
      • Received: 1 March 2017
      Published in toit Volume 18, Issue 2

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