Artificial intelligence-enhanced zero-knowledge proofs for privacy-preserving digital forensics in cloud environments

Abstract

This paper proposed an Artificial Intelligence (AI) enhanced Zero Knowledge Proofs (ZKPs) based comprehensive framework used to improve security, privacy, scalability and efficiency in forensic investigations for the multi-cloud environment, a growing concern for cybersecurity and digital forensics domains. With the growing invulnerability of data storage and inefficient processing in cloud computing landscapes, forensic investigations confront privacy preservation, data integrity, and interoperability issues amongst various cloud providers. Despite existing frameworks, there are few adaptive solutions that holistically solve these challenges. To address such issues and challenges, we propose a suite of frameworks, including an Adaptive Multi-Cloud Forensic Integration Framework (A-MCFIF), Multi-Factor Access Control Framework (MACF), Adaptive ZKP Optimization Framework (AZOF), and Privacy Enhanced Data Security Framework (PDSF) to bridge this gap. Incorporating AI-enhanced ZKP and Multi-Factor Authentication (MFA), these frameworks secure data and improve the efficiency of proof generation and verification while meeting privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Our extensive evaluation of the proposed framework included computing efficiency, memory consumption, data handling efficiency, scalability, overall performance, and cost-effectiveness. We also analyse verification latency to assess the framework’s real-time processing capabilities, which overcome existing solutions. Furthermore, our research includes cloud-specific threat models such as insider threats and data breaches and shows the benefits of the proposed framework for counteracting these risks by proving mathematical and empirical security against privacy breaches. Finally, we bring new insights and contribute to the development of secure, privacy-compliant, and efficient forensic processes, which are elaborated as a comprehensive solution for more reconstructive forensic initiatives in increasingly sophisticated cloud environments.