Securing AI Models and Data PDF

Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Securing AI Models and Data  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected]...

Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Securing AI Models and Data  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Introduction: Securing AI Models and Data Securing AI models and data involves protecting them from unauthorized access, manipulation, and theft using encryption, access controls, secure machine learning techniques, and monitoring. Ensuring compliance with privacy regulations helps mitigate cybersecurity risks and maintains the integrity, confidentiality, and availability of AI systems.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Secure Training of AI Models Secure Training of AI Models involves techniques to protect the integrity, confidentiality, and robustness of AI models during training. This includes safeguarding training data, algorithms, and resulting models from unauthorized access, tampering, and malicious activities to ensure the models are trustworthy and resilient against attacks.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Secure Training of AI Modelsity and Confidentiality of AI Training Process: Data Encryption: Encrypt training data both at rest and in transit. Use strong encryption algorithms (e.g., AES-256) to protect data confidentiality. Implement secure key management practices to safeguard encryption keys. Secure Computing Environments: Utilize secure and isolated computing environments for training. Employ hardware security measures (e.g., Trusted Platform Modules) to prevent unauthorized access. Implement containerization and virtualization to isolate training processes. Access Controls: Implement strict access controls and authentication mechanisms. Use role-based access control (RBAC) to limit access to training data and model parameters. Monitor and audit access logs for suspicious activities.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Federated Learning Security Federated Learning Security refers to the measures and techniques implemented to ensure the security and privacy of the federated learning process. Federated learning is a decentralized approach to training machine learning models where data remains on local devices, and only model updates are shared and aggregated. Federated learning security focuses on protecting the data, the model updates, and the overall training process from threats such as data breaches, tampering, and adversarial attacks.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Federated Learning Securityng Environment and Ensuring Security: Secure Communication: Encrypt data during transmission between client devices and the central server. Usee protocols like TLS (Transport Layer Security) to secure communication channels. Privacy-Preserving Techniques: Employ differential privacy to anonymize contributions from individual client devices. Implement techniques such as federated averaging to aggregate model updates without exposing raw data. Threat Mitigation: Monitor for data poisoning and model inversion attacks. Implement anomaly detection to identify malicious behaviors within federated learning.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Homomorphic Encryption for AI Homomorphic encryption (HE) is an advanced cryptographic technique that allows computations to be performed on encrypted data, producing results that, when decrypted, match those obtained by performing the same operations on the unencrypted data. This enables secure computation on sensitive data without revealing the data itself, which is particularly valuable in AI applications.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Technique Using Encryption to Perform Secure Computation on Encrypted Data: Homomorphic Encryption Basics: Encrypt data so computations can be performed directly on encrypted data. Types: Partially homomorphic encryption (e.g., RSA), fully homomorphic encryption (e.g., FHE). Applications in AI: Securely compute AI models on encrypted data, maintaining data privacy. Protect sensitive information during AI model training and inference. Challenges and Considerations: Overhead in computational complexity and performance. Selecting appropriate encryption schemes based on application requirements.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Homomorphic Encryption for AI Types of Homomorphic Encryption: Fully Homomorphic Encryption (FHE): Supports both addition and multiplication operations on encrypted data. Enables complex computations without decrypting the data at any point. Examples: IBM's HElib, Microsoft's SEAL. Partially Homomorphic Encryption (PHE): Supports only one type of operation (either addition or multiplication). Examples: Paillier (addition), RSA (multiplication).  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Differential Privacy in AI Differential privacy aims to provide formal guarantees about the privacy of individual data points within a dataset, allowing meaningful data analysis without compromising individual privacy.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Implementation of Differential Privacy to Protect Individual Data Points: Differential Privacy Principles: Add noise to query results to mask individual contributions. Control privacy budget to balance utility and privacy preservation. Applications in AI: Use in data aggregation processes during training to prevent inference of individual data points. Balance privacy guarantees with model accuracy and utility.  Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] Techniques: Local differential privacy: Perturb data before aggregation. Global differential privacy: Perturb aggregated results to maintain privacy. Guimaras State University COLLEGE OF SCIENCE AND TECHNOLOGY Alaguisoc, Jordan, Guimaras [email protected] THANK YOU!