A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters

Author:

Ashok Vikas G.,Mukkamala Ravi

Publisher

Springer Berlin Heidelberg

Reference10 articles.

1. Liu, K., Kargupta, H., Ryan, J.: Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans. Knowledge and Data Engg. 18(1), 92–106 (2006)

2. Lecture Notes in Computer Science;Y. Lindell,2000

3. Lecture Notes in Computer Science;M. Kantarcioglu,2009

4. Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), November 19-22. IEEE Computer Society, Los Alamitos (2003)

5. Bloom, B.H.: Space/time Trade-offs in Hash coding with Allowable Errors. Communications of the ACM 13(7), 422–426 (1970)

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