Privacy Challenges in Edge Computing: Addressing privacy concerns and challenges arising from data processing at the edge of the network
Keywords:
Edge Computing, Privacy Challenges, Data SecurityAbstract
Edge computing has emerged as a paradigm to process data closer to the source, reducing latency and bandwidth usage. However, this shift raises significant privacy concerns. This paper investigates the privacy challenges in edge computing, analyzing the implications of data processing at the network's periphery. We identify key privacy threats, including data leakage, unauthorized access, and inadequate security measures. We also discuss existing privacy-preserving techniques and propose future research directions to mitigate these challenges. Our findings emphasize the importance of integrating privacy considerations into edge computing frameworks to ensure data protection and user trust.
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Prabhod, Kummaragunta Joel. "Advanced Techniques in Reinforcement Learning and Deep Learning for Autonomous Vehicle Navigation: Integrating Large Language Models for Real-Time Decision Making." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 1-20.
Tatineni, Sumanth, and Sandeep Chinamanagonda. “Leveraging Artificial Intelligence for Predictive Analytics in DevOps: Enhancing Continuous Integration and Continuous Deployment Pipelines for Optimal Performance”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Feb. 2021, pp. 103-38, https://aimlstudies.co.uk/index.php/jaira/article/view/104.
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