Integrating Technical and Legal Concepts of Privacy
Open accessAbstract
We provide an overview of legal and technical concepts of privacy protection. Data protection guarantees exist in European Union at a primary law level since 2009, when the Lisbon Treaty has become effective. The new regulation called general data protection regulation adopted on April 27, 2016 will enter into effect on May 25, 2018. Regarding data protection, the US follows a very different approach. The cornerstone of the technical framework of data protection is the concept of differential privacy for which the privacy’s provable guarantees hold even against worst-case scenarios when the access to arbitrary auxiliary information is provided. Research directions for integrating legal and technical concepts of privacy are discussed. Although both fields, legal and technical, approach privacy from different perspectives, we expect that these approaches will converge to a common framework. The privacy-preserving framework, therefore, should be a balance between: 1) ambiguous legal concepts and precise mathematical notions of privacy; 2) the desired privacy level; 3) the information loss, which is measured by data utility metrics; and 4) the complexity and the practical feasibility of the proposed technique. Key notions of fairness are incompatible with each other, and hence any privacy-preserving framework should also include the trade-offs between conditions imposed in the definitions of fairness. The complex assembly of legal and technical components implementing various privacy requirements in the privacy-preserving framework, should be realized within the privacy-by-design concept.
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Many variables are explicitly defined as 'sensitive' by specific legal frameworks.
“We note that many variables are explicitly deined as łsensitivež by speciic legal frameworks, see [25,129,130,191,192,209,268,280,306] and the references therein.”
From Fairness in machine learning: A survey (2024) · cites this paper as [268]
There is a tension between advancing the fairness state of the art, privacy, and policy.
“There is a tension here between advancing the fairness state of the art, privacy [111,268], and policy.”
From Fairness in machine learning: A survey (2024) · cites this paper as [268]
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Bartolini, C., Calabrò, A., & Marchetti, E. (2019) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0007392304210428 · Cited by 17 · see citing works
Blockchain and Homomorphic Encryption for Data Security and Statistical Privacy
Raj, R., Peker, Y. K., & Mutlu, Z. D. (2024) · Electronics · doi:10.3390/electronics13153050 · Cited by 16 · see citing works
Distributed Differential Privacy via Shuffling Versus Aggregation: A Curious Study
Wei, Y. X., Jia, J., Wu, Y., Hu, C., Dong, C., Liu, Z., Chen, X., Peng, Y., & Wang, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3351474 · Cited by 14 · see citing works
Calvi, A., Malgieri, G., & Kotzinos, D. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659024 · Cited by 15 · see citing works
Towards a Triad for Data Privacy
Covert, Q., Steinhagen, D., Francis, M. A., & Streff, K. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.535 · Cited by 12 · see citing works
Privacy by Evidence: A Methodology to develop privacy-friendly software applications
Barbosa, P., Brito, A., & de Almeida, H. O. (2019) · Information Sciences · doi:10.1016/j.ins.2019.09.040 · Cited by 9 · see citing works
Saad, A. M. H., & Myat, A. (2025) · AIP conference proceedings · doi:10.1063/5.0289756 · Cited by 6 · see citing works
Lin, Y., Lemos, M., & Neuschaefer‐Rube, C. (2022) · JMIR Medical Education · doi:10.2196/34042 · Cited by 6 · see citing works
Using Differential Privacy to Define Personal, Anonymous, and Pseudonymous Data
Huang, T., & Zheng, S. (2023) · IEEE Access · doi:10.1109/access.2023.3321578 · Cited by 6 · see citing works
Privacy Design Strategies and the GDPR: A Systematic Literature Review
Saltarella, M., Desolda, G., & Lanzilotti, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77392-2_16 · Cited by 6 · see citing works
A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems
Borson, J. E., & Xu, H. (2022) · IEEE Access · doi:10.1109/access.2022.3221409 · Cited by 6 · see citing works
Stakeholders’ understanding of data privacy: implications for digital credit consumer
Koul, S., Verma, R., & Ajaygopal, K. V. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2568200 · Cited by 4 · see citing works
An Inventory of International Privacy Principles: A 14 Country Analysis
Francis, M. A., Covert, Q., Steinhagen, D., & Streff, K. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.534 · Cited by 3 · see citing works
Data quantitative and qualitative study in Brazilian Open Data Portals
Carmo, S. L. O. D., Geyer, C. F. R., & dos Anjos, J. C. S. (2024) · Journal of Internet Services and Applications · doi:10.5753/jisa.2024.3980 · Cited by 1 · see citing works
Dissanayake, P. (2023) · doi:10.26686/wgtn.24580947
- Authors
- Sokolovska, A., & Kocarev, L.
- Affiliations
- Macedonian Academy of Sciences and Arts (MK)
- ORCID
- Kocarev, Ljupco
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 26543-26557
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2804248130
- Cited by
- 24
- References
- 108
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 1 · 2020: 4 · 2019: 3
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- ONR and ONR Global (N62909-16-1 2222)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- privacy preservation, data protection law, General Data Protection Regulation, differential privacy, privacy by design, fairness trade-offs, data utility, EU data protection
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