Abstract

Edge computing enhances data processing efficiency by minimizing latency and bandwidth usage, but its decentralized nature creates severe privacy concerns. The traditional security methodologies are ineffective in edge environments, so robust privacy frameworks are critical to ensure data confidentiality, integrity, and regulatory compliance. This paper presents various privacy-preserving techniques, including cryptographic methods, federated learning, access control mechanisms, and anonymization schemes. Cryptographic techniques such as homomorphic encryption and secure multi-party computation enable secure processing of data, and federated learning minimizes data exposure in AI-driven applications. Access control frameworks such as role-based and zero-trust models prevent unauthorized access, and anonymization techniques minimize re-identification threats. Edge-to-cloud privacy frameworks and secure communication protocols also ensure data security.