The rapid growth of neural networks has expanded into sectors such as healthcare, finance, and automation. However, alongside these advancements, there are significant data security and privacy concerns. Neural network models often rely on continuous training and large datasets, making them vulnerable to data breaches. Traditional anonymization techniques, such as k-anonymity and l-diversity, frequently fail against advanced re-identification methods, model inversion, and membership inference attacks.
Through this teaching aid, readers can understand essential factors related to privacy-preserving techniques in neural networks, including understanding and mitigating privacy risks such as data re-identification, model inversion, and membership inference attacks. They will gain insights into anonymization strategies, federated learning (FL), and differential privacy (DP) and how these methods help protect user’s private data while training model. Additionally, readers will explore regulatory compliance with laws like GDPR and CCPA, learning how AI systems must adapt to evolving data privacy requirements. Lastly, through analyzing real-world applications and case studies, readers will develop the ability to assess the vulnerabilities of AI models, implement privacy-enhancing techniques, and evaluate trade-offs between privacy and model accuracy, equipping them with a comprehensive understanding of privacy concerns in machine learning and AI development.
Group Members:
Harshad Krishnaraj (Student ID 30125318)
Md. Saidul Arifin Shuvo (Student ID – 30259582)
Shah Zaib (Student ID – 30270945)
Teaching Aide:
Walkthrough:
Discussion Questions:
- What are the pros and cons of federated learning for privacy?
- What are some real-world examples of AI privacy failures?
- What are the biggest limitations of privacy-preserving AI techniques today?
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