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  • IMPROVE

    Rights statement: ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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IMPROVE: Identifying Minimal PROfile VEctors for similarity based access control

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published
Publication date23/08/2016
Host publication2016 IEEE Trustcom/BigDataSE/I​SPA
PublisherIEEE
Pages868-875
Number of pages8
ISBN (electronic)9781509032051
ISBN (print)9781509032068
<mark>Original language</mark>English
Event15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16) - Tianjin, China
Duration: 23/08/2016 → 26/08/2016
http://adnet.tju.edu.cn/TrustCom2016/

Conference

Conference15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16)
Abbreviated titleIEEE TrustCom 16
Country/TerritoryChina
CityTianjin
Period23/08/16 → 26/08/16
Internet address

Publication series

Name2016 IEEE Trustcom/BigDataSE/I​SPA
PublisherIEEE
ISSN (electronic)2324-9013

Conference

Conference15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16)
Abbreviated titleIEEE TrustCom 16
Country/TerritoryChina
CityTianjin
Period23/08/16 → 26/08/16
Internet address

Abstract

There is ample evidence which shows that social media users struggle to make appropriate access control decisions while disclosing their information and smarter mechanisms are needed to assist them. Using profile information to ascertain similarity between users and provide suggestions to them during the process of making access control decisions has been put forth as a possible solution to this problem. This paper presents an empirical study aimed at identifying the minimal subset of attributes which are most suitable for being used to create profile vectors for the purpose of predicting access control decisions. We begin with an exhaustive list of 30 profile attributes and identify a subset of 2 profile attributes which are shown to be sufficient in obtaining similarity between profiles and predicting access control decisions with the same accuracy as previous models. We demonstrate that using this pair of attributes will help mitigate the challenges encountered by similarity based access control mechanisms.

Bibliographic note

©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.