SURVEY PAPER ON DATAPRIVACY IN AI: BALANCING PRIVACY WITH ETHICS

Artificial Intelligence (AI) has become a transformative force across industries, enabling automation, predictive analytics, and efficient decision-making. As AI applications grow, so do concerns about the ethical implications of data usage and potential privacy violations (Floridi & Cowls, 2019). Ensuring responsible AI deployment requires balancing innovation with rigorous data governance frameworks (European Commission, 2021). This paper aims to provide insights into emerging privacy-preserving AI methodologies while addressing regulatory and ethical challenges. However, the integration of AI into daily life presents significant data privacy challenges. AI systems process vast amounts of personal data, leading to concerns about consent, security, and regulatory compliance. This paper explores the evolving landscape of data privacy in AI, analyzing challenges such as data collection risks, biases in AI models, security breaches, and compliance with global regulations like GDPR and CCPA (Dwork & Roth, 2014). Various privacy-preserving AI solutions, including federated learning, differential privacy, homomorphic encryption, and secure multi-party computation, are examined in depth. Additionally, ethical AI frameworks and privacy-preserving machine learning approaches are discussed to ensure responsible AI deployment. Recent research advancements in privacy-preserving AI models, including privacy-aware machine learning algorithms and zero-knowledge proofs, are also explored. Real-world case studies, including the Facebook-Cambridge Analytica scandal, illustrate the consequences of inadequate data privacy measures (World Economic Forum, 2023). The paper concludes by highlighting the need for collaborative efforts between policymakers, researchers, and technologists to develop robust privacy-preserving AI models that balance innovation with ethical considerations.