Personalised Privacy by Default Preferences - Experiment and Analysis

Toru Nakamura, Shinsaku Kiyomoto, Welderufael B. Tesfay, Jetzabel Serna

2016

Abstract

In this paper, we present a novel mechanism that provides individuals with personalised privacy by default setting when they register into a new system or service. The proposed approach consists of an intelligent mechanism that learns users’ context and preferences to generate personalised default privacy settings. To achieve this, we used a machine learning approach that requires a minimal number of questions at the registration phase, and, based on users’ responses, sets up privacy settings associated to users’ privacy preferences for a particular service. This is the first attempt to predict general privacy preferences from a minimal number of questions. We propose two approaches. The first scheme is based on the sole use of SVM to predict users’ personalised settings. The second scheme implemented an additional layer that includes clustering. The accuracy of proposed approaches is evaluated by comparing the guessed answers against the answers from a questionnaire administered to 10,000 participants. Results show that, the SVM based scheme is able to guess the the full set of personalised privacy settings with an accuracy of 85%, by using a limited input of only 5 answers from the user.

Paper Citation

in Harvard Style

Nakamura T., Kiyomoto S., Tesfay W. and Serna J. (2016). Personalised Privacy by Default Preferences - Experiment and Analysis . In Proceedings of the 2nd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-167-0, pages 53-62. DOI: 10.5220/0005681100530062

in Bibtex Style

@conference{icissp16,
author={Toru Nakamura and Shinsaku Kiyomoto and Welderufael B. Tesfay and Jetzabel Serna},
title={Personalised Privacy by Default Preferences - Experiment and Analysis},
booktitle={Proceedings of the 2nd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2016},
pages={53-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005681100530062},
isbn={978-989-758-167-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Personalised Privacy by Default Preferences - Experiment and Analysis
SN - 978-989-758-167-0
AU - Nakamura T.
AU - Kiyomoto S.
AU - Tesfay W.
AU - Serna J.
PY - 2016
SP - 53
EP - 62
DO - 10.5220/0005681100530062