A Novel Integrated Privacy Preserving Framework for Secure Data-Driven Artificial Intelligence Systems
Tasneem Hossain
Arizona State University
https://orcid.org/0009-0001-0205-7276
Keywords: Artificial Intelligence, Privacy Framework, Data Encryption, Access Control, Federated Learning, Homomorphic Encryption, Auditing, Continuous Improvement
Abstract
The rapid advancement and widespread adoption of artificial intelligence (AI) systems across various domains have raised significant concerns regarding the privacy of sensitive data processed by these systems. This research proposes a novel privacy framework that integrates five key components to address the challenges of ensuring privacy in AI systems. The framework includes robust data encryption and anonymization techniques, secure access control and authentication mechanisms, secure AI model training and deployment methods, auditing and monitoring processes, and an emphasis on regular improvement and collaboration.
The proposed framework introduces and combines the state-of-the-art encryption algorithms, such as AES-256 and RSA-2048, with anonymization techniques, including k-anonymity, l-diversity, and differential privacy. This ensures the confidentiality and privacy of sensitive data throughout the AI system lifecycle. Role-based access control (RBAC) and attribute-based access control (ABAC) mechanisms, with multi-factor authentication (MFA) and secure authentication protocols, are incorporated to enforce strict access control and prevent unauthorized access to sensitive information. A key innovation of this framework is the integration of secure AI model training and deployment techniques. Federated learning is employed to enable collaborative model training on distributed datasets without centralizing sensitive data, while secure enclaves and trusted execution environments (TEEs) are used to protect models during training and inference. Homomorphic encryption and secure multi-party computation (SMPC) are joined in this framework to enable computations on encrypted data. The framework also suggest the importance of regular auditing, monitoring, and incident response. Robust logging and auditing mechanisms, anomaly detection, and intrusion detection systems (IDS) are proposed to be implemented to identify potential security breaches and privacy violations. Regular security audits and penetration testing are recommended to be conducted to proactively identify and address vulnerabilities. Well-defined incident response plans and procedures are established to ensure prompt and effective handling of privacy breaches or security incidents. To ensure the long-term effectiveness and relevance of the privacy framework, a focus is also placed on regular improvement and collaboration. Regular privacy risk assessments and updating privacy measures are suggested as needed to align with existing regulations and best practices.
