AI-Enhanced Zero-Knowledge Proofs for Privacy-Preserving Cloud Forensics
Organized by: International Forensic Scientist Awards
Website: forensicscientist.org
14th Edition of Forensic Scientist Awards 26-27 September 2025 | Mumbai, India
Introduction
As cloud computing becomes increasingly central to business and personal data storage, ensuring secure and privacy-compliant forensic investigations is a growing challenge. Traditional forensic methods often struggle with data privacy, integrity, and interoperability across multi-cloud environments. Recent advances in Artificial Intelligence (AI) and cryptography, particularly Zero-Knowledge Proofs (ZKPs), offer a promising solution to these challenges.
The Challenge
Multi-cloud ecosystems present unique difficulties for forensic investigations. Data is often distributed across various providers, making it difficult to maintain privacy while verifying evidence. Current frameworks offer limited solutions, leaving gaps in scalability, efficiency, and regulatory compliance with standards like GDPR and HIPAA.
Our Solution
Our research introduces an AI-enhanced ZKP framework tailored for privacy-preserving digital forensics in cloud environments. This framework integrates several specialized components:
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Adaptive Multi-Cloud Forensic Integration Framework (A-MCFIF): Streamlines data handling and interoperability across different cloud providers.
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Multi-Factor Access Control Framework (MACF): Ensures secure access to sensitive forensic data using advanced authentication methods.
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Adaptive ZKP Optimization Framework (AZOF): Enhances the efficiency of proof generation and verification.
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Privacy-Enhanced Data Security Framework (PDSF): Safeguards data while complying with regulatory standards.
Key Benefits:
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Enhanced Security: AI-powered ZKPs ensure sensitive data remains private during verification.
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Improved Efficiency: Optimized proof generation reduces verification time and resource consumption.
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Scalability: The framework adapts to large-scale cloud environments.
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Compliance: Meets GDPR, HIPAA, and other privacy regulations.
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Threat Mitigation: Addresses insider threats, data breaches, and other cloud-specific risks.
Results & Insights
Extensive evaluation demonstrates the framework’s efficiency in memory usage, data handling, performance, and cost-effectiveness. Verification latency tests show real-time capabilities surpassing existing solutions. Both mathematical and empirical analyses confirm the framework’s robustness against privacy breaches.
Conclusion
By combining AI with Zero-Knowledge Proofs and multi-factor authentication, this framework offers a comprehensive, privacy-preserving solution for digital forensic investigations in cloud environments. It empowers organizations to conduct secure, scalable, and regulatory-compliant investigations while minimizing risk—a significant step forward in cloud cybersecurity and forensic research.
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