Cloud Security in Edge Computing: Addressing Data Privacy Concerns

Abstract

The integration of cloud and edge computing has revolutionized data processing by enabling low-latency and efficient handling of data at the network's edge. However, this shift has introduced significant data privacy challenges, particularly due to the decentralized nature of edge computing. This paper explores the core security concerns related to data privacy in cloud-edge ecosystems, focusing on the vulnerabilities introduced by the distribution of data across edge nodes. Current cloud security practices, such as encryption and access control, are assessed for their effectiveness in securing edge environments. The paper also examines existing privacy-preserving technologies, such as federated learning and lightweight encryption, and identifies gaps in their application to edge computing. In response, the study proposes decentralized security models and enhanced data privacy mechanisms tailored to the unique requirements of edge networks. Through a review of case studies in IoT, healthcare, and autonomous systems, this paper offers practical insights into improving data privacy in edge computing environments.