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Bridging the gap between GPDR and software development: the MATERIALIST framework.

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Title: Bridging the gap between GPDR and software development: the MATERIALIST framework.
Authors: Saltarella, Marco1 (AUTHOR) marco.saltarella@finconsgroup.com, Desolda, Giuseppe2 (AUTHOR) giuseppe.desolda@uniba.it, Esposito, Andrea2 (AUTHOR) andrea.esposito@uniba.it, Greco, Francesco2 (AUTHOR) francesco.greco@uniba.it, Lanzilotti, Rosa2 (AUTHOR) rosa.lanzilotti@uniba.it
Source: Multimedia Tools & Applications. Jun2025, Vol. 84 Issue 20, p22971-23010. 40p.
Subjects: Data privacy, Systems software, Computer software quality control, Computer software development, Data protection
Abstract: As software production evolves, privacy is becoming an increasingly important consideration. This is especially true as national and supranational regulations, such as GDPR, require privacy as a mandatory aspect of software development. However, challenges such as a lack of knowledge about privacy and data protection regulations hinder the adoption of effective and compliant privacy implementation mechanisms. To address this issue, this article presents MATERIALIST, a methodological and technological framework that supports stakeholders involved in a software development lifecycle in including GDPR in their activities. Specifically, it provides design patterns that can be selected starting from GDPR articles, code vulnerabilities, and software lifecycle phases. The framework aims to facilitate the adoption of appropriate privacy implementation mechanisms in the software development lifecycle, thereby improving software quality. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Bridging the gap between GPDR and software development: the MATERIALIST framework.
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  Data: <searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+software%22">Systems software</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink>
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  Data: As software production evolves, privacy is becoming an increasingly important consideration. This is especially true as national and supranational regulations, such as GDPR, require privacy as a mandatory aspect of software development. However, challenges such as a lack of knowledge about privacy and data protection regulations hinder the adoption of effective and compliant privacy implementation mechanisms. To address this issue, this article presents MATERIALIST, a methodological and technological framework that supports stakeholders involved in a software development lifecycle in including GDPR in their activities. Specifically, it provides design patterns that can be selected starting from GDPR articles, code vulnerabilities, and software lifecycle phases. The framework aims to facilitate the adoption of appropriate privacy implementation mechanisms in the software development lifecycle, thereby improving software quality. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Jun2025
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