SoK: Enhancing Privacy-Preserving Software Development from a Developers' Perspective
April 29, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tharaka Wijesundara, Matthew Warren, Nalin Asanka Gamagedara Arachchilage
arXiv ID
2504.20350
Category
cs.SE: Software Engineering
Cross-listed
cs.CR
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In software development, privacy preservation has become essential with the rise of privacy concerns and regulations such as GDPR and CCPA. While several tools, guidelines, methods, methodologies, and frameworks have been proposed to support developers embedding privacy into software applications, most of them are proofs-of-concept without empirical evaluations, making their practical applicability uncertain. These solutions should be evaluated for different types of scenarios (e.g., industry settings such as rapid software development environments, teams with different privacy knowledge, etc.) to determine what their limitations are in various industry settings and what changes are required to refine current solutions before putting them into industry and developing new developer-supporting approaches. For that, a thorough review of empirically evaluated current solutions will be very effective. However, the existing secondary studies that examine the available developer support provide broad overviews but do not specifically analyze empirically evaluated solutions and their limitations. Therefore, this Systematic Literature Review (SLR) aims to identify and analyze empirically validated solutions that are designed to help developers in privacy-preserving software development. The findings will provide valuable insights for researchers to improve current privacy-preserving solutions and for practitioners looking for effective and validated solutions to embed privacy into software development.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted