Privacy-preserving collaborative filtering using randomized perturbation techniques
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.
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.
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.
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