A TOPSIS-Based Approach to Evaluate Alternative Solutions for GDPR-Compliant Smart-City Services Implementation

Conference Campanile, Lelio and Iacono, Mauro and Mastroianni, Michele and Riccio, Christian and Viscardi, Bruna — 2026 · Lecture Notes in Computer Science

Venue & metadata

Journal/Proceedings: Lecture Notes in Computer Science
Volume: 15893 LNCS
Pages: 303 – 316
Note: Cited by: 0
Author keywords: Data management; Decision support; GDPR; Information systems; Performance evaluation; Privacy; Queuing networks; Risk Analysis; Risk Assessment

Abstract

Adapting or designing a system which operates on personal data in EU is impacted by the privacy-by-design and privacy-by-default principles because of the prescriptions of the GDPR. In this paper we propose an approach to decision making which is based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The approach is applied to a GDPR system compliance design process, based on a case study about system performance evaluation by means of queuing networks, but is absolutely general with respect to analogous problems, in which cost issues should be balanced with technical performances and risk exposure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Computer network performance evaluation GS Decision making GS Information management GS Information systems GS Information use GS Risk analysis GS Risk management GS Alternative solutions GS Decision supports GS GDPR GS Performances evaluation GS Privacy GS Queuing network GS Risk analyze GS Risks assessments GS Solution based approaches GS Technique for order preference by similarities to ideal solutions GS Decision support systems GS Risk assessment GS

Links & artifacts

DOI Publisher

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Suggested citation

Campanile, L., Iacono, M., Mastroianni, M., Riccio, C., & Viscardi, B. (2026). A TOPSIS-Based Approach to Evaluate Alternative Solutions for GDPR-Compliant Smart-City Services Implementation [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 303–316. https://doi.org/10.1007/978-3-031-97645-2_20

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