Skip to main content

A Data-Reachability Model for Elucidating Privacy and Security Risks Related to the Use of Online Social Networks

Creese, Sadie, Goldsmith, Michael, Nurse, Jason R. C., Phillips, Elizabeth (2012) A Data-Reachability Model for Elucidating Privacy and Security Risks Related to the Use of Online Social Networks. In: 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications. . pp. 1124-1131. IEEE ISBN 978-1-4673-2172-3. (doi:10.1109/TrustCom.2012.22) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Author's Accepted Manuscript
Restricted to Repository staff only
Contact us about this Publication Download (185kB)
[img]
Official URL
http://dx.doi.org/10.1109/TrustCom.2012.22

Abstract

Privacy and security within Online Social Networks (OSNs) has become a major concern over recent years. As individuals continue to actively use and engage with these mediums, one of the key questions that arises pertains to what unknown risks users face as a result of unchecked publishing and sharing of content and information in this space. There are numerous tools and methods under development that claim to facilitate the extraction of specific classes of personal data from online sources, either directly or through correlation across a range of inputs. In this paper we present a model which specifically aims to understand the potential risks faced should all of these tools and methods be accessible to a malicious entity. The model enables easy and direct capture of the data extraction methods through the encoding of a data-reachability matrix for which each row represents an inference or data-derivation step. Specifically, the model elucidates potential linkages between data typically exposed on social-media and networking sites, and other potentially sensitive data which may prove to be damaging in the hands of malicious parties, i.e., fraudsters, stalkers and other online and offline criminals. In essence, we view this work as a key method by which we might make cyber risk more tangible to users of OSNs.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/TrustCom.2012.22
Subjects: Q Science
T Technology
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Security Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Jason Nurse
Date Deposited: 02 Jul 2018 16:00 UTC
Last Modified: 01 Aug 2019 10:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67528 (The current URI for this page, for reference purposes)
Nurse, Jason R. C.: https://orcid.org/0000-0003-4118-1680
  • Depositors only (login required):

Downloads

Downloads per month over past year