Recommender Systems and  their Security Concerns

Jun Wang; Qiang Tang

Paper 2015/1108

Recommender Systems and their Security Concerns

Jun Wang and Qiang Tang

Abstract

Instead of simply using two-dimensional $U s e r \times I t e m$ features, advanced recommender systems rely on more additional dimensions (e.g. time, location, social network) in order to provide better recommendation services. In the first part of this paper, we will survey a variety of dimension features and show how they are integrated into the recommendation process. When the service providers collect more and more personal information, it brings great privacy concerns to the public. On another side, the service providers could also suffer from attacks launched by malicious users who want to bias the recommendations. In the second part of this paper, we will survey attacks from and against recommender service providers, and existing solutions.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Preprint. MINOR revision.
Keywords: recommender system robustness privacy homomorphic encryption multiparty computation
Contact author(s): qiang tang @ uni lu
History: 2015-11-20: revised; 2015-11-18: received; See all versions
Short URL: https://ia.cr/2015/1108
License: CC BY

BibTeX

@misc{cryptoeprint:2015/1108,
      author = {Jun Wang and Qiang Tang},
      title = {Recommender Systems and  their Security Concerns},
      howpublished = {Cryptology {ePrint} Archive, Paper 2015/1108},
      year = {2015},
      url = {https://eprint.iacr.org/2015/1108}
}