Conference Paper

Towards Efficient Privacy-Preserving Collaborative Recommender Systems

Heinz Sch., Carnegie Mellon Univ., Pittsburgh, PA
DOI: 10.1109/GRC.2008.4664769 Conference: The 2008 IEEE International Conference on Granular Computing, GrC 2008, Hangzhou, China, 26-28 August 2008
Source: DBLP

ABSTRACT Recommender systems use various types of information to help customers find products of personalized interest. To increase the usefulness of recommender systems in certain circumstances, it could be desirable to merge recommender system databases between companies, thus expanding the data pool. This can lead to privacy disclosure hazards that this paper addresses by constructing an efficient privacy-preserving collaborative recommender system based on the scalar product protocol.

0 Bookmarks
 · 
98 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Automatic recommender systems have become a cornerstone of e-commerce, especially after the great welcome of Web 2.0 based on participation and interaction of Internet users. Collaborative Filtering (CF) is a recommender system that is becoming increasingly relevant for the industry due to the growth of the Internet, which has made it much more difficult to effectively extract useful information. In this paper, we introduce a taxonomy of the different CF families and we discuss the most relevant Privacy Preserving Collaborative Filtering (PPCF) methods in the literature. To understand the inherent challenges of the PPCF, we also conduct an overview of the current tendencies and major drawbacks of this kind of recommender systems, and we propose several strategies to overcome the shortcomings.
    ICEBE, Coventry; 01/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Collaborative Filtering (CF) is a recommender system which is becoming increasingly relevant for the industry. Current research focuses on Privacy Preserving Collaborative Filtering (PPCF), whose aim is to solve the privacy issues raised by the systematic collection of private information. In this paper, we propose a new micro aggregation-based PPCF method that distorts data to provide k-anonymity, whilst simultaneously making accurate recommendations. Experimental results demonstrate that the proposed method perturbs data more efficiently than the well-known and widely used distortion method based on Gaussian noise addition.
    e-Business Engineering (ICEBE), 2013 IEEE 10th International Conference on; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while providing predictions, various schemes have been proposed to estimate recommendations without deeply jeopardizing privacy. Such methods eliminate or reduce data owners' privacy, financial, and legal concerns by employing different privacy-preserving techniques. Although there are considerable numbers of studies focusing on privacy-preserving collaborative filtering schemes, there is no comprehensive survey investigating them with respect to different directions. In this survey, we mainly focus on studying various privacy-preserving recommendation methods according to the data partitioning cases and the utilized techniques for preserving confidentiality. We also review privacy in general and examine in collaborative filtering scenarios. We discuss the proposed schemes in terms of their limitations and practical implementation challenges. Moreover, we give an overview of evaluation of such schemes. We finally provide a comprehensive guideline for studying in this area and propose future research directions.
    International Journal of Software Engineering and Knowledge Engineering 02/2014; 23(08). DOI:10.1142/S0218194013500320 · 0.26 Impact Factor

Full-text (2 Sources)

Download
24 Downloads
Available from
Jun 5, 2014