Abstract
This research focuses on data privacy and security in ChatGPT systems, which have gained popularity in various industries. It aims to identify potential risks and propose effective strategies to ensure data privacy and security, fostering user trust. The chapter explores privacy-preserving techniques like differential privacy, federated learning (FL), secure multi-party computation, and homomorphic encryption to mitigate risks. Compliance with data protection regulations, for example, CCPA and GDPR, is essential for ensuring data privacy. Implementing a secure infrastructure with encryption, data access controls, and regular security audits strengthens the overall security posture. User awareness and consent are also crucial, with transparent data collection and usage policies, informed consent, and opt-out mechanisms. A well-structured incident response plan, communication strategies, and learning from security breaches enhance system resilience. The chapter presents case studies and best practices for secure ChatGPT systems, drawing insights from past privacy failures.
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