Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques

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	Similar Articles Articles by Huseyin Polat Articles by Wenliang Du

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Huseyin Polat, Wenliang Du, "Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques," Data Mining, IEEE International Conference on, pp. 625, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250993, author = {Huseyin Polat and Wenliang Du}, title = {Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {625}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250993}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques SN - 0-7695-1978-4 SP EP A1 - Huseyin Polat, A1 - Wenliang Du, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Huseyin Polat, Syracuse University, NY

Wenliang Du, Syracuse University, NY

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250993

Collaborative Filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers is not an easy task because many customers are so concerned about their privacy that they might decide to give false information. We propose a randomized perturbation (RP) technique to protect users' privacy while still producing accurate recommendations.

Citation:

Huseyin Polat, Wenliang Du, "Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques," icdm, pp.625, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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