Evaluation of the Impact of Privacy-Enhancing Technologies on Data Security Compliance in Regulated Frameworks
Y H Xu · International Journal of High Speed Electronics and Systems · 2025
Ensuring data privacy and regulatory compliance has become increasingly complex with the proliferation of sensitive data and the evolution of data protection laws such as GDPR and CCPA. Privacy-Enhancing Technologies (PETs) offer a promising solution, but many existing methods struggle to simultaneously satisfy efficiency, scalability, and evolving compliance requirements. To address these limitations, we propose a hybrid compliance framework that integrates adaptive differential privacy with advanced cryptographic mechanisms. Our method dynamically adjusts privacy budgets based on data sensitivity, while preserving computational efficiency through secure multiparty computation and homomorphic encryption. This adaptive mechanism ensures that sensitive information is protected without compromising data utility or analytic capability. Experimental results on multiple regulatory datasets demonstrate that our approach significantly outperforms conventional PET frameworks in both privacy guarantees and compliance accuracy. By aligning privacy protection with regulatory demands, our method enables practical, scalable, and legally interpretable data security solutions for high-risk domains such as finance, healthcare, and cloud platforms.