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Evaluating PIT-OSN 1 in inspecting the privacy levels of an online social network

Published: 22 October 2019 Publication History

Abstract

In the last few years, Online Social Networks (OSNs) have experienced a growth in their number of users, becoming an increasingly embedded part of people's daily lives. Privacy expectations in OSNs are getting smaller as more members begin to face potential privacy problems when interacting with these systems. Inspections methods can be an effective alternative for addressing privacy problems because they allow detecting a possible defect that could be causing the system to behave in an undesirable way. Based on that, a set of privacy inspection techniques called PIT-OSN (Privacy Inspection Techniques for Online Social Network) was proposed. This paper focuses on one of these techniques (the PIT-OSN 1), which supports the inspection of the privacy levels in an OSN. The goal of this paper is to present the evaluation of PIT-OSN 1, through an empirical study, which collected quantitative and qualitative data. Results obtained from the analysis indicate that the technique assists nonexpert inspectors detecting privacy problems effectively and that it was considered easy to use and useful by the participants of the study. Finally, the qualitative analysis points out relevant improvement opportunities in PIT-OSN 1.

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      cover image ACM Other conferences
      IHC '19: Proceedings of the 18th Brazilian Symposium on Human Factors in Computing Systems
      October 2019
      679 pages
      ISBN:9781450369718
      DOI:10.1145/3357155
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 22 October 2019

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      Author Tags

      1. empirical study
      2. privacy evaluation
      3. privacy inspection
      4. social networks
      5. user privacy

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      IHC '19: XVIII Brazilian Symposium on Human Factors in Computing Systems
      October 22 - 25, 2019
      Espírito Santo, Vitória, Brazil

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      IHC '19 Paper Acceptance Rate 56 of 165 submissions, 34%;
      Overall Acceptance Rate 331 of 973 submissions, 34%

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