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Characterizations of Local Recoding Method on k-Anonymity

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Advances in Network-Based Information Systems (NBiS 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 22))

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Abstract

k-Anonymity is one of the most widely used techniques for protecting the privacy of the publishing datasets by making each individual not distinguished from at least k-1 other individuals. The local recoding method is an approach to achieve k-anonymization through suppression and generalization. The method generalizes the dataset at the cell level. Therefore, the local recoding could achieve the k-anonymization with only a small distortion. As the optimal k-anonymity has been proved as the NP-hard problem, the plenty of optimal algorithm local recoding has been proposed. In this research, we study the characteristics of the local recoding method. In addition, we discover the special characteristic dataset that all generalization hierarchies of each quasi-identifier are identical, called an “Identical Generalization Hierarchy” (IGH) data. We also compare the efficiency of the well-known algorithms of the local recoding method on both \(non-IGH\) and IGH data.

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References

  1. Aggarwal, G., Panigrahy, R., Feder, T., Thomas, D., Kenthapadi, K., Khuller, S., Zhu, A.: Achieving anonymity via clustering. ACM Trans. Algorithms 6(3), 49:1–49:19 (2010)

    Article  MathSciNet  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. IEEE Computer Society, Washington, DC (2005)

    Google Scholar 

  3. Byun, J.W., Kamra, A., Bertino, E., Li, N.: Efficient k-anonymization using clustering techniques. In: Proceedings of the 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, pp. 188–200. Springer, Heidelberg (2007)

    Google Scholar 

  4. Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

  5. El Emam, K., Brown, A., AbdelMalik, P.: Evaluating predictors of geographic area population size cut-offs to manage re-identification risk. J. Am. Med. Inform. Assoc. 16(2), 256–266 (2009)

    Article  Google Scholar 

  6. El Emam, K., Dankar, F., Issa, R., Jonker, E., Amyot, D., Cogo, E., Corriveau, J.P., Walker, M., Chowdhury, S., Vaillancourt, R., Roffey, T., Bottomley, J.: A globally optimal k-anonymity method for the de-identification of health data. J. Am. Med. Inform. Assoc. JAMIA 16, 670–82 (2009)

    Article  Google Scholar 

  7. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001)

    Article  Google Scholar 

  8. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015)

    Article  Google Scholar 

  9. 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. ACM, New York (2005)

    Google Scholar 

  10. LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Mondrian multidimensional k-anonymity. In: 22nd International Conference on Data Engineering (ICDE 2006), p. 25 (2006)

    Google Scholar 

  11. 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. ACM, New York (2004)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  15. Sweeney, L.: k -anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 1–14 (2002)

    MathSciNet  MATH  Google Scholar 

  16. Sweeney, L.A.: Computational disclosure control: a primer on data privacy protection. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2001). AAI0803469

    Google Scholar 

  17. Wong, R.C.W., Li, J., Fu, A.W.C., Wang, K.: (\(\alpha \), k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 754–759. ACM, New York (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Chiang Mai University, Chiang Mai, Thailand

    Waranya Mahanan & Juggapong Natwichai

  2. Department of Industrial Engineering, University of Arkansas, Arkansas, USA

    W. Art Chaovalitwongse

Authors
  1. Waranya Mahanan
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  2. Juggapong Natwichai
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  3. W. Art Chaovalitwongse
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Corresponding author

Correspondence to Waranya Mahanan .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia

    Natalia Kryvinska

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  4. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Mahanan, W., Natwichai, J., Art Chaovalitwongse, W. (2019). Characterizations of Local Recoding Method on k-Anonymity. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_56

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