Synonyms
Homogeneously distributed data
Definition
Data is said to be horizontally partitioned when several organizations own the same set of attributes for different sets of entities. More formally, horizontal partitioning of data can be defined as follows: given a dataset DB = (E, I) (e.g., hospital discharge data for state of Texas) where E is the set of entities about whom the information is collected (e.g., the set of patients) and I is the set of attributes that is collected about entities (e.g., set of features collected about patients), DB is said to be horizontally partitioned among k sites where each site owns DBi = (Ei, Ii), 1 ≤ i ≤ k if E = E1 ∪ E2…∪ Ek, Ei ∩ Ej = ∅, 1 ≤ i ≠ j ≤ k and I = I1 = I2… = In. In relational terms, with horizontal partitioning, the relation to be mined is the union of the relations at the sites.
Historical Background
Cheap data storage and abundant network capacity have revolutionized data collection and data dissemination. At the same time,...
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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.
Friedman A, Wolff R, Schuster A. Providing k-anonymity in data mining. VLDB J. 2008;17(4):789–804.
Han J, Kamber M. Data mining: concepts and techniques. San Francisco: Morgan Kaufmann; 2000.
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.
Kantarcioglu M, Vaidya J. Privacy preserving naive bayes classifier for horizontally partitioned data. In: Proceedings of the Workshop on Privacy Preserving Data Mining; 2003.
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.
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Lin X, Clifton C, Zhu M. Privacy preserving clustering with distributed EM mixture modeling. Knowl Inform Syst. 2005;8(1):68–81.
Lindell Y, Pinkas B. Privacy preserving data mining. In: Advances in Cryptology: Proceedings of the 20th Annual International Cryptology Conference; 2000. p. 36–54.
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.
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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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