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Horizontally Partitioned Data

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Recommended Reading

  1. Agrawal R, Srikant R.. Privacy-preserving data mining. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 439–50.

    Google Scholar 

  2. Clifton C, Marks D. Security and privacy implications of data mining. In: Proceedings of the Workshop on Data Mining and Knowledge Discovery; 1996. p. 15–9.

    Google Scholar 

  3. Friedman A, Wolff R, Schuster A. Providing k-anonymity in data mining. VLDB J. 2008;17(4):789–804.

    Article  Google Scholar 

  4. Han J, Kamber M. Data mining: concepts and techniques. San Francisco: Morgan Kaufmann; 2000.

    MATH  Google Scholar 

  5. Jagannathan G, Wright R.N. Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2005. p. 593–9.

    Google Scholar 

  6. Kantarcioglu M, Vaidya J. Privacy preserving naive bayes classifier for horizontally partitioned data. In: Proceedings of the Workshop on Privacy Preserving Data Mining; 2003.

    Google Scholar 

  7. Kantarcıoğlu M, Clifton C. Privately computing a distributed k-nn classifier. In: Proceedings of the 8th European Conference on Principles of Data Mining And Knowledge Discovery; 2004. p. 279–0.

    Google Scholar 

  8. Kantarcıoğlu M, Jin J, Clifton C. When do data mining results violate privacy? In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004; p. 599–604.

    Google Scholar 

  9. Lin X, Clifton C, Zhu M. Privacy preserving clustering with distributed EM mixture modeling. Knowl Inform Syst. 2005;8(1):68–81.

    Article  Google Scholar 

  10. Lindell Y, Pinkas B. Privacy preserving data mining. In: Advances in Cryptology: Proceedings of the 20th Annual International Cryptology Conference; 2000. p. 36–54.

    Chapter  Google Scholar 

  11. Yu H, Jiang X, Vaidya J. Privacy-preserving svm using nonlinear kernels on horizontally partitioned data. In: Proceedings of the 2006 ACM Symposium on Applied Computing; 2006. p. 603–10.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, USA

    Murat Kantarcıoğlu

Authors
  1. Murat Kantarcıoğlu
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Corresponding author

Correspondence to Murat Kantarcıoğlu .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Chris Clifton

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Kantarcıoğlu, M. (2018). Horizontally Partitioned Data. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1391

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