Combating Misinformation in AI-Generated Content: Effective Mitigation Strategies
Keywords:
AI Privacy, Differential Privacy, Federated Learning, Data Protection, Homomorphic Encryption, Privacy-Preserving AI, Inference Attacks, Secure Machine LearningAbstract
The rapid advancements in Artificial Intelligence (AI) have significantly improved various sectors, including healthcare, finance, and communication. However, these developments have also raised concerns about data privacy violations, as AI systems collect, process, and analyze massive volumes of sensitive personal information. Unauthorized data access, inference attacks, and unethical data usage pose serious risks to individuals and organizations. This research paper explores mitigation strategies for AI-enabled privacy violations by investigating existing solutions, proposing advanced methods, and presenting experimental results to validate the effectiveness of different approaches. Techniques such as differential privacy, homomorphic encryption, federated learning, and adversarial training are examined in-depth. The findings indicate that a multi-layered security framework is essential to mitigating privacy threats effectively. The study concludes that while AI-driven privacy violations present complex challenges, proactive governance, ethical AI deployment, and robust technical safeguards can significantly reduce risks.
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