Access Restriction

Author Vaidya, J. ♦ Shafiq, B. ♦ Basu, A. ♦ Yuan Hong
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Training ♦ Privacy ♦ Data privacy ♦ Sensitivity ♦ Naive Bayes Classification ♦ Noise ♦ Differential Privacy ♦ Standards
Abstract Privacy and security concerns often prevent the sharing of users' data or even of the knowledge gained from it, thus deterring valuable information from being utilized. Privacy-preserving knowledge discovery, if done correctly, can alleviate this problem. One of the most important and widely used data mining techniques is that of classification. We consider the model where a single provider has centralized access to a dataset and would like to release a classifier while protecting privacy to the best extent possible. Recently, the model of differential privacy has been developed which provides a strong privacy guarantee even if adversaries hold arbitrary prior knowledge. In this paper, we apply this rigorous privacy model to develop a Naive Bayes classifier, which is often used as a baseline and consistently provides reasonable classification performance. We experimentally evaluate the proposed approach, and discuss how it could be potentially deployed in PaaS clouds.
Description Author affiliation: Rutgers, State Univ. of New Jersey, Newark, NJ, USA (Vaidya, J.) || SUNY - Univ. at Albany, Suny, Albany, NY, USA (Yuan Hong) || Lahore Univ. of Manage. Sci., Lahore, Pakistan (Shafiq, B.) || KDDI R&D Labs., Fujimino, Japan (Basu, A.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-11-17
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9780769551456
Size (in Bytes) 638.85 kB
Page Count 6
Starting Page 571
Ending Page 576

Source: IEEE Xplore Digital Library