• Pavan Kalyan Konakalla

Generative Artificial Intelligence (AI) practices are complex in nature, and this dissertation focuses on the security and privacy aspects of the same utilizing two case studies, including generative AI in vehicular networks and generative AI in education, particularly on ChatGPT. It starts by outlining what is meant by generative AI technologies, which include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), then examines the potential application of generative AI technologies in improving vehicular navigation, traffic forecasting as well as in the generation of content for educational use. Some concerns raised are the need to process data in real time, decision-making in complex and fast-changing environments, and the security issue of users' data. Comparative analysis across both case studies delves into strategies for mitigating these risks: introducing and implementing a multimodality semantic-aware architecture for vehicular networks using big data to enhance data credibility and performance and integrating deep reinforcement learning (DRL) strategies in the vehicular networks to boost the performance of data; also, using Analytic Hierarchy Process (AHP) approaches in education to solve some ethical issues like copyright infringement, privacy issues, and cheating. Paving all these findings amid the consensus highlights generalizable themes that underscore the centrality of security and privacy precautions; further, they highlight the imperative of capturing comprehensive ethical frameworks that can effectively govern the integration of AI across the given sectors. The consequences for the field highlight the possibility of and the need for transformative AI technologies along with collaborations between the disciplines and all the stakeholders involved. In the context of the presented study's conclusion, the author outlines future research directions in optimizing the real-time decision-making algorithm, transforming the adaptive security model, and discovering AI opportunities across industries with a focus on healthcare, finance, and more, with a specific task of integrating the positive AI potentials with the concerns and values of the repercussions and keeping up with the growing challenges of the AI environment.

Keywords— Generative Artificial Intelligence (Generative AI), Security, Privacy

"SECURITY AND PRIVACY CONCERNS IN GENERATIVE AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g784-g790, July-2024, Available :http://www.jetir.org/papers/JETIR2407689.pdf

"SECURITY AND PRIVACY CONCERNS IN GENERATIVE AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg784-g790, July-2024, Available at : http://www.jetir.org/papers/JETIR2407689.pdf