Perturbation Based Privacy Preserving Slope One Predictors for Collaborative Filtering - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

Perturbation Based Privacy Preserving Slope One Predictors for Collaborative Filtering

(1) , (2) , (3)
1
2
3
Anirban Basu
  • Function : Author
  • PersonId : 1007390
Jaideep Vaidya
  • Function : Author
  • PersonId : 978061

Abstract

The prediction of the rating that a user is likely to give to an item, can be derived from the ratings of other items given by other users, through collaborative filtering (CF). However, CF raises concerns about the privacy of the individual user’s rating data. To deal with this, several privacy-preserving CF schemes have been proposed. However, they are all limited either in terms of efficiency or privacy when deployed on the cloud. Due to its simplicity, Lemire and MacLachlan’s weighted Slope One predictor is very well suited to the cloud. Our key insight is that, the Slope One predictor, being an invertible affine transformation, is robust to certain types of noise. We exploit this fact to propose a random perturbation based privacy preserving collaborative filtering scheme. Our evaluation shows that the proposed scheme is both efficient and preserves privacy.
Fichier principal
Vignette du fichier
978-3-642-29852-3_2_Chapter.pdf (322.45 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01517652 , version 1 (03-05-2017)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Anirban Basu, Jaideep Vaidya, Hiroaki Kikuchi. Perturbation Based Privacy Preserving Slope One Predictors for Collaborative Filtering. 6th International Conference on Trust Management (TM), May 2012, Surat, India. pp.17-35, ⟨10.1007/978-3-642-29852-3_2⟩. ⟨hal-01517652⟩
25 View
105 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More