IMPROVE-Identifying minimal profile vectors for similarity based access control

Gaurav Misra, Jose M. Such, Hamed Balogun

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

13 Citations (Scopus)

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 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.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages868-875
Number of pages8
ISBN (Electronic)9781509032051
DOIs
Publication statusPublished - 9 Feb 2017
EventJoint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016 - Tianjin, China
Duration: 23 Aug 201626 Aug 2016

Publication series

NameProceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

Conference

ConferenceJoint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
Country/TerritoryChina
CityTianjin
Period23/08/1626/08/16

Keywords

  • Machine learning
  • Privacy
  • Social computing

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