Efficient Privacy-Preserving Collaborative Filtering
Based on the Weighted Slope One Predictor
Anirban Basu1, Jaideep Vaidya2, and
Hiroaki Kikuchi3
1Graduate School of Engineering, Tokai University,
2-3-23 Takanawa, Minato-ku, Tokyo 108-8619, Japan
abasu@cs.dm.u-tokai.ac.jp
2MSIS Department, Rutgers, The State University of New
Jersey
1, Washington Park, Newark, New Jersey, 07102-1897, USA
jsvaidya@business.rutgers.edu
3Graduate School of Engineering, Tokai University,
1117, Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
kikn@tokai.ac.jp
Abstract
Rating-based collaborative filtering (CF) predicts the
rating that a user will give to an item, derived
from the ratings of other items given by other users.
Such CF schemes utilise either user neighbourhoods
(i.e. user-based CF) or item neighbourhoods (i.e.
item-based CF). Lemire and MacLachlan
[19] proposed three related schemes for an item-based CF
with predictors of the form f (x) = x+b,
hence the name ¡°slope one¡±. Slope One predictors have
been shown to be accurate on large datasets.
They also have several other desirable properties such as
being updatable on the fly, efficient to compute,
and work even with sparse input. In this paper, we
present a privacy-preserving item-based
CF scheme through the use of an additively homomorphic
public-key cryptosystem on the weighted
Slope One predictor; and show its applicability on both
horizontal and vertical partitions, and include
a discussion on arbitrary partitions as well. We present an
evaluation of our proposed scheme
in terms of communication and computation complexity,
performance of cryptographic primitives
and performance of a single-partition, single machine
implementation in 64-bit Java.
Keywords: Privacy Preserving, Slope One,
Collaborative Filtering
Journal
of Internet Services and Information Security (JISIS), 1(4): 26-46, November 2011 [pdf]