Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

Angeliki Kitsiou, Eleni Tzortzaki, Christos Kalloniatis and Stefanos Gritzalis
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Angeliki Kitsiou: Privacy Engineering and Social Informatics Laboratory, Department of Cultural Technology and Communication, University of the Aegean, GR 81100 Lesvos, Greece
Eleni Tzortzaki: Information and Communication Systems Security Laboratory, Deptartment of Information and Communication Systems Engineering, University of the Aegean, GR 83200 Samos, Greece
Christos Kalloniatis: Privacy Engineering and Social Informatics Laboratory, Department of Cultural Technology and Communication, University of the Aegean, GR 81100 Lesvos, Greece
Stefanos Gritzalis: Laboratory of Systems Security, Department of Digital Systems, University of Piraeus, GR 18532 Piraeus, Greece

Future Internet, 2021, vol. 13, issue 2, 1-25

Abstract: Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

Keywords: social networks; self-adaptive privacy; privacy related requirements; users’ social landscape (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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