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Anonymization of Data Sets with NULL Values

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIV

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9510))

Abstract

Releasing, publishing or transferring microdata is restricted by the necessity to protect the privacy of data owners. k-anonymity is one of the most widespread concepts for anonymizing microdata but it does not explicitly cover NULL values which are nevertheless frequently found in microdata. We study the problem of NULL values (missing values, non-applicable attributes, etc.) for anonymization in detail, present a set of new definitions for k-anonymity explicitly considering NULL values and analyze which definition protects from which attacks. We show that an adequate treatment of missing values in microdata can be easily achieved by an extension of generalization algorithms. In particular, we show how the proposed treatment of NULL values was incorporated in the anonymization tool ANON, which implements generalization and tuple suppression with an application specific definition of information loss. With a series of experiments we show that NULL aware generalization algorithms have less information loss than standard algorithms.

The work was supported by Austrian Ministry of Science and Research within the project BBMRI.AT (GZ 10.470/0016-II/3/2013) and Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) within the project ANON.

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References

  1. Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Approximation algorithms for \(k\)-anonymity. In: Proceedings of the International Conference on Database Theory, ICDT 2005 (2005)

    Google Scholar 

  2. Bayardo, R.J., Agrawal, R.: Data privacy through optimal \(k\)-anonymization. In: Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, pp. 217–228 (2005)

    Google Scholar 

  3. Ciglic, M., Eder, J., Koncilia, C.: ANON - a flexible tool for achieving optimal \(k\)-anonymous and \(\ell \)-diverse tables. Technical report, University of Klagenfurt (2014). http://isys.uni-klu.ac.at/PDF/2014-ANON-Techreport.pdf

  4. Ciglic, M., Eder, J., Koncilia, C.: \(k\)-anonymity of microdata with NULL values. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014, Part I. LNCS, vol. 8644, pp. 328–342. Springer, Heidelberg (2014)

    Google Scholar 

  5. Ciriani, V., De Capitani di Vimercati, S., Foresti, S., Samarati, P.: \(k\)-anonymity. In: Yu, T., Jajodia, S. (eds.) SDMDS 2007. AISC, vol. 33, pp. 323–353. Springer, New York (2007)

    Chapter  Google Scholar 

  6. Codd, E.F.: Extending the database relational model to capture more meaning. ACM Trans. Database Syst. 4(4), 397–434 (1979)

    Article  Google Scholar 

  7. Cox, L.H.: Suppression methodology and statistical disclosure control. J. Am. Stat. Assoc. 75(370), 377–385 (1980)

    Article  MATH  Google Scholar 

  8. Eder, J., Dabringer, C., Schicho, M., Stark, K.: Information systems for federated biobanks. In: Hameurlain, A., Küng, J., Wagner, R. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems I. LNCS, vol. 5740, pp. 156–190. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Eder, J., Gottweis, H., Zatloukal, K.: IT solutions for privacy protection in biobanking. Public Health Genomics 15(5), 254–262 (2012)

    Article  Google Scholar 

  10. Eder, J., Stark, K., Asslaber, M., Abuja, P.M., Gottweis, H., Trauner, M., Mischinger, H.J., Schippinger, W., Berghold, A., Denk, H., Zatloukal, K.: The genome austria tissue bank. Pathobiology 74(4), 251–8 (2007)

    Article  Google Scholar 

  11. Frank, A., Asuncion, A.: UCI machine learning repository (2010). http://archive.ics.uci.edu/ml

  12. Fung, B.C.M., Wang, K., Fu, A.W.-C., Yu, P.S.: Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques, 1st edn. Chapman & Hall/CRC, Boca Raton (2010)

    Book  Google Scholar 

  13. Gaskell, G., Gottweis, H., Starkbaum, J., Gerber, M.M., Broerse, J., Gottweis, U., Hobbs, A., Ilpo, H., Paschou, M., Snell, K., Soulier, A.: Publics and biobanks: Pan-European diversity and the challenge of responsible innovation. Eur. J. Hum. Genet. 21(1), 14–20 (2013)

    Article  Google Scholar 

  14. ISO: ISO/IEC 9075–2:2011 Information technology – Database languages – SQL – Part 2: Foundation (SQL/Foundation), December 2011

    Google Scholar 

  15. Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 279–288 (2002)

    Google Scholar 

  16. Kifer, D., Gehrk, J.: Injecting utility into anonymized datasets. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of data, SIGMOD 2006, pp. 217–228 (2006)

    Google Scholar 

  17. LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain \(k\)-anonymity. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of data, SIGMOD 2005, pp. 49–60 (2005)

    Google Scholar 

  18. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond \(k\)-anonymity. ACM Trans. Knowl. Disc. Data (TKDD) 1(1), 3 (2007)

    Article  Google Scholar 

  19. Matthews, G.J., Harel, O.: Data confidentiality: a review of methods for statistical disclosure limitation and methods for assessing privacy. Stat. Surv. 5, 1–29 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  20. Meyden, R.: Logical approaches to incomplete information: a survey. In: Chomicki, J., Saake, G. (eds.) Logics for Databases and Information Systems. The Springer International Series in Engineering and Computer Science, vol. 436, pp. 307–357. Springer, New York (1998). Chapter 10

    Chapter  Google Scholar 

  21. Meyerson, A., Williams, R:. On the complexity of optimal \(k\)-anonymity. In: Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2004, pp. 223–228 (2004)

    Google Scholar 

  22. Ohrn, A., Ohno-Machado, L.: Using boolean reasoning to anonymize databases. Artif. Intell. Med. 15(3), 235–254 (1999)

    Article  Google Scholar 

  23. Park, H., Shim, K.: Approximate algorithms for \(k\)-anonymity. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of data, SIGMOD 2007, pp. 67–78 (2007)

    Google Scholar 

  24. Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. (TKDE) 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  25. Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (abstract). In: Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1998, p. 188 (1998)

    Google Scholar 

  26. Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: \(k\)-anonymity and its enforcement through generalization and suppression. Technical report (1998)

    Google Scholar 

  27. Stark, K., Eder, J., Zatloukal, K.: Priority-based \(k\)-anonymity accomplished by weighted generalisation structures. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 394–404. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  28. Stark, K., Eder, J., Zatloukal, K.: Achieving \(k\)-anonymity in datamarts used for gene expressions exploitation. J. Integr. Bioinform. 4(1), 57 (2007)

    Google Scholar 

  29. Sun, X., Wang, H., Li, J., Truta, T.M.: Enhanced p-sensitive \(k\)-anonymity models for privacy preserving data publishing. Trans. Data Priv. 1(2), 53–66 (2008)

    MathSciNet  Google Scholar 

  30. Sweeney, L.: Achieving \(k\)-anonymity privacy protection using generalization and suppression. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 10(5), 571–588 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  31. Terrovitis, M., Mamoulis, N., Kalnis, P.: Local and global recoding methods for anonymizing set-valued data. VLDB J. 20(1), 83–106 (2011)

    Article  Google Scholar 

  32. Tian, H., Zhang, W.: Extending l-diversity to generalize sensitive data. Data Knowl. Eng. 70(1), 101–126 (2011)

    Article  Google Scholar 

  33. Wichmann, H.-E.E., Kuhn, K.A., Waldenberger, M., Schmelcher, D., Schuffenhauer, S., Meitinger, T., Wurst, S.H., Lamla, G., Fortier, I., Burton, P.R., Peltonen, L., Perola, M., Metspalu, A., Riegman, P., Landegren, U., Taussig, M.J., Litton, J.-E.E., Fransson, M.N., Eder, J., Cambon-Thomsen, A., Bovenberg, J., Dagher, G., van Ommen, G.-J.J., Griffith, M., Yuille, M., Zatloukal, K.: Comprehensive catalog of European biobanks. Nat. Biotechnol. 29(9), 795–797 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Margareta Ciglic, Johann Eder & Christian Koncilia

Authors
  1. Margareta Ciglic
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  2. Johann Eder
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  3. Christian Koncilia
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Corresponding author

Correspondence to Johann Eder .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. Universidad Politécnica de Valencia, Valencia, Spain

    Hendrik Decker

  5. Czech Technical University, Prague, Czech Republic

    Lenka Lhotska

  6. University of Auckland, Auckland, New Zealand

    Sebastian Link

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Ciglic, M., Eder, J., Koncilia, C. (2016). Anonymization of Data Sets with NULL Values. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIV. Lecture Notes in Computer Science(), vol 9510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49214-7_7

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  • DOI: https://doi.org/10.1007/978-3-662-49214-7_7

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