Abstract
The emergence of electric business facilitates people in purchasing merchandises over the Internet. To sell the products better, online service providers use recommender systems to provide recommendations to customers. Most recommender systems are based on collaborative filtering (CF) technique. This technique provides recommendations based on users’ transaction history. Due to the technical limitations, many online merchants ask a third party to help develop and maintain recommender systems instead of doing that themselves. Therefore, they need to share their data with these third parties and users’ private information is prone to leaking. Furthermore, the fast data growth should be handled by the data owner efficiently without sacrificing privacy. In this chapter, we propose a privacy preserving data updating scheme for collaborative filtering purpose and study its performance on two different datasets. The experimental results show that the proposed scheme does not degrade recommendation accuracy and can preserve a satisfactory level of privacy while updating the data efficiently.
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Notes
- 1.
\(\Delta \hat{R}_{k}^{\prime }(m+1:m+p,:)\) is a Matlab notation that means the last \(p\) rows of \(\Delta \hat{R}_{k}^{\prime }\)
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Wang, X., Zhang, J. (2013). Handling the Data Growth with Privacy Preservation in Collaborative Filtering. In: Yang, GC., Ao, Sl., Gelman, L. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6190-2_18
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DOI: https://doi.org/10.1007/978-94-007-6190-2_18
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