Exploring how experienced and unexperienced professionals use a privacy threat modeling methodology

Authors

DOI:

https://doi.org/10.5753/jis.2023.3235

Keywords:

Threat Modeling, Privacy Threat, Online Social Network, Empirical Study

Abstract

Online Social Networks (OSNs) have become one of the principal technological phenomena of the Web, gaining an eminent popularity among its users. With the growing worldwide expansion of OSN services, people have devoted time and effort to maintaining and manipulating their online identity on these systems. However, the processing of personal data through these networks has exposed users to various privacy threats. Consequently, new solutions need to be developed for addressing the threat scenarios to which a user is potentially exposed. In this sense, this paper proposes PTMOL (Privacy Threat MOdeling Language), an approach for modeling privacy threats in OSN domain. The proposed language aims to support the capture, organization and analysis of specific privacy threats that a user is exposed to when sharing assets in a social application, also enabling the definition of countermeasures to prevent or mitigate the effects of threat scenarios. The first language version has undergone a preliminary empirical study that identified its validity as a modeling language. The results indicate that the use of the language is potentially useful for identifying real privacy threats due to its exploratory and reflexive nature. We expect to contribute to support designers in making more preemptive decisions about user privacy risk, helping them to introduce privacy early in the development cycle of social applications.

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Published

2023-07-29

How to Cite

RODRIGUES, A.; VILLELA, M. L.; FEITOSA, E. Exploring how experienced and unexperienced professionals use a privacy threat modeling methodology. Journal on Interactive Systems, Porto Alegre, RS, v. 14, n. 1, p. 274–291, 2023. DOI: 10.5753/jis.2023.3235. Disponível em: https://sol.sbc.org.br/journals/index.php/jis/article/view/3235. Acesso em: 1 oct. 2023.

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Section

Regular Paper