TY - GEN
T1 - Privacy-preserving collaborative filtering using randomized perturbation techniques
AU - Polat, Huseyin
AU - Du, Wenliang
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=46749122242&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=46749122242&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:46749122242
SN - 0769519784
SN - 9780769519784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 625
EP - 628
BT - Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
T2 - 3rd IEEE International Conference on Data Mining, ICDM '03
Y2 - 19 November 2003 through 22 November 2003
ER -