GDPRShield: AI-Powered GDPR Support for Software Developers in Small and Medium-Sized Enterprises
- URL: http://arxiv.org/abs/2505.12640v2
- Date: Wed, 21 May 2025 07:24:34 GMT
- Title: GDPRShield: AI-Powered GDPR Support for Software Developers in Small and Medium-Sized Enterprises
- Authors: Tharaka Wijesundara, Mathew Warren, Nalin Arachchilage,
- Abstract summary: This paper introduces a novel AI-powered framework called "ShieldShield" specifically designed to enhance awareness of SME software developers.<n>"ShieldShield" boosts developers motivation to comply with data violations from early stages of software development.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the rapid increase in privacy violations in modern software development, regulatory frameworks such as the General Data Protection Regulation (GDPR) have been established to enforce strict data protection practices. However, insufficient privacy awareness among SME software developers contributes to failure in GDPR compliance. For instance, a developer unfamiliar with data minimization may build a system that collects excessive data, violating GDPR and risking fines. One reason for this lack of awareness is that developers in SMEs often take on multidisciplinary roles (e.g., front-end, back-end, database management, and privacy compliance), which limits specialization in privacy. This lack of awareness may lead to poor privacy attitudes, ultimately hindering the development of a strong organizational privacy culture. However, SMEs that achieve GDPR compliance may gain competitive advantages, such as increased user trust and marketing value, compared to others that do not. Therefore, in this paper, we introduce a novel AI-powered framework called "GDPRShield," specifically designed to enhance the GDPR awareness of SME software developers and, through this, improve their privacy attitudes. Simultaneously, GDPRShield boosts developers' motivation to comply with GDPR from the early stages of software development. It leverages functional requirements written as user stories to provide comprehensive GDPR-based privacy descriptions tailored to each requirement. Alongside improving awareness, GDPRShield strengthens motivation by presenting real-world consequences of noncompliance, such as heavy fines, reputational damage, and loss of user trust, aligned with each requirement. This dual focus on awareness and motivation leads developers to engage with GDPRShield, improving their GDPR compliance and privacy attitudes, which will help SMEs build a stronger privacy culture over time.
Related papers
- DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries [0.0]
This paper introduces Differentially Private Resilient Temporal Federated Learning (DP-RTFL)<n>It is designed to ensure training continuity, precise state recovery, and strong data privacy.<n>The framework is particularly suited for critical applications like credit risk assessment using sensitive financial data.
arXiv Detail & Related papers (2025-05-27T16:30:25Z) - Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG)<n>FedE4RAG facilitates collaborative training of client-side RAG retrieval models.<n>We apply homomorphic encryption within federated learning to safeguard model parameters.
arXiv Detail & Related papers (2025-04-27T04:26:02Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions [0.0]
This paper introduces the Privacy-Preserving Zero-Shot Learning (PP-ZSL) framework, a novel approach leveraging large language models (LLMs) in a zero-shot learning mode.<n>Unlike conventional machine learning methods, PP-ZSL eliminates the need for local training on sensitive data by utilizing pre-trained LLMs to generate responses directly.<n>The framework incorporates real-time data anonymization to redact or mask sensitive information, retrieval-augmented generation (RAG) for domain-specific query resolution, and robust post-processing to ensure compliance with regulatory standards.
arXiv Detail & Related papers (2024-12-10T17:20:47Z) - Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy [55.357715095623554]
Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties.
We propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification.
arXiv Detail & Related papers (2024-10-24T03:39:55Z) - An Exploratory Mixed-Methods Study on General Data Protection Regulation (GDPR) Compliance in Open-Source Software [4.2610816955137]
European Union's General Data Protection Regulation require software developers to meet privacy requirements interacting with users' data.
Prior research describes impact of such laws on development, but only when commercial software.
arXiv Detail & Related papers (2024-06-20T20:38:33Z) - A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
We propose a new differential privacy paradigm called estimate-verify-release (EVR)
EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output.
Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
arXiv Detail & Related papers (2023-04-17T00:38:01Z) - NL2GDPR: Automatically Develop GDPR Compliant Android Application
Features from Natural Language [28.51179772165298]
NL2 is an information extraction tool developed by Baidu Cognitive Computing Lab.
It generates privacycentric information and generating privacy policies.
It can achieve 92.9% identification of policies related to personal storage process, data process, and types respectively.
arXiv Detail & Related papers (2022-08-29T04:16:50Z) - Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data.
FL and related techniques are often described as privacy-preserving.
We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
arXiv Detail & Related papers (2021-12-21T08:44:05Z) - Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance [5.37556626581816]
Multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems.<n> centralized training, the most common paradigm for MARL, limits large-scale deployment in communication-constrained cloud-edge environments.<n>Our proposed approach is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of households.
arXiv Detail & Related papers (2021-10-06T14:05:26Z) - PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework
Based on Adversarial Learning [111.19576084222345]
This paper proposes a framework of Privacy-preserving Credit risk modeling based on Adversarial Learning (PCAL)
PCAL aims to mask the private information inside the original dataset, while maintaining the important utility information for the target prediction task performance.
Results indicate that PCAL can learn an effective, privacy-free representation from user data, providing a solid foundation towards privacy-preserving machine learning for credit risk analysis.
arXiv Detail & Related papers (2020-10-06T07:04:59Z) - Why are Developers Struggling to Put GDPR into Practice when Developing
Privacy-Preserving Software Systems? [3.04585143845864]
General Data Protection Law provides guidelines for developers on how to protect user data.
Previous research has attempted to investigate what hinders developers from embedding privacy into software systems.
This paper investigates the issues that hinder software developers from implementing software applications taking law on-board.
arXiv Detail & Related papers (2020-08-07T04:34:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.