Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes

Dirk van der Linden; Irit Hadar; Matthew Edwards; Awais Rashid

doi:https://doi.org/10.1007/978-3-030-55958-8_3

Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes

Dirk van der Linden, Irit Hadar, Matthew Edwards, Awais Rashid

Department of Information Systems

University of Haifa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Understanding developers’ attitudes towards handling personal data is vital in order to understand whether the software they create handles their users’ privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users’ privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers’ attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale’s deployment, we further discuss mismatches between developers’ attitudes and their self-perceived extent of properly handling their users’ privacy, and the importance of understanding developers’ attitudes towards data monetization.

Original language	American English
Title of host publication	Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers
Editors	Thomas Groß, Theo Tryfonas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	47-65
Number of pages	19
ISBN (Print)	9783030559571
DOIs	https://doi.org/10.1007/978-3-030-55958-8_3
State	Published - 2021
Event	9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019 - Luxembourg, Luxembourg Duration: 26 Sep 2019 → 26 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11739 LNCS

Conference

Conference	9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019
Country/Territory	Luxembourg
City	Luxembourg
Period	26/09/19 → 26/09/19

Keywords

Attitude
Developer
Privacy
Scale development

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

https://doi.org/10.1007/978-3-030-55958-8_3

Cite this

van der Linden, D., Hadar, I., Edwards, M., & Rashid, A. (2021). Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. In T. Groß, & T. Tryfonas (Eds.), Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers (pp. 47-65). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11739 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-55958-8_3

van der Linden, Dirk ; Hadar, Irit ; Edwards, Matthew et al. / Data, Data, Everywhere : Quantifying Software Developers’ Privacy Attitudes. Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. editor / Thomas Groß ; Theo Tryfonas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 47-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Understanding developers{\textquoteright} attitudes towards handling personal data is vital in order to understand whether the software they create handles their users{\textquoteright} privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users{\textquoteright} privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers{\textquoteright} attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale{\textquoteright}s deployment, we further discuss mismatches between developers{\textquoteright} attitudes and their self-perceived extent of properly handling their users{\textquoteright} privacy, and the importance of understanding developers{\textquoteright} attitudes towards data monetization.",

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van der Linden, D, Hadar, I, Edwards, M & Rashid, A 2021, Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. in T Groß & T Tryfonas (eds), Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11739 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 47-65, 9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019, Luxembourg, Luxembourg, 26/09/19. https://doi.org/10.1007/978-3-030-55958-8_3

Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. / van der Linden, Dirk; Hadar, Irit; Edwards, Matthew et al.
Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. ed. / Thomas Groß; Theo Tryfonas. Springer Science and Business Media Deutschland GmbH, 2021. p. 47-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11739 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Edwards, Matthew

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N2 - Understanding developers’ attitudes towards handling personal data is vital in order to understand whether the software they create handles their users’ privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users’ privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers’ attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale’s deployment, we further discuss mismatches between developers’ attitudes and their self-perceived extent of properly handling their users’ privacy, and the importance of understanding developers’ attitudes towards data monetization.

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van der Linden D, Hadar I, Edwards M, Rashid A. Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. In Groß T, Tryfonas T, editors, Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 47-65. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-030-55958-8_3

Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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