Abstract:

The combination of Internet of Things (IoT) devices and edge computing has transformed data processing by minimizing latency and enhancing efficiency. But with it comes equally important privacy concerns, especially with sensitive information processed close to its source. Currently, methods to secure IoT data tend to leverage conventional encryption measures, which imply decryption at the edge, putting the data itself at risk for breaches and at the expense of privacy. For bridging these deficits, we put forth a new framework named Homomorphic Encryption-based Privacy Preservation Framework (HEPPF), based on homomorphic encryption methods used in the paradigm of edge computing. HEPPF is established with the view of ensuring end-to-end privacy as it facilitates the edge nodes to carry out computations on data encrypted without reading the plaintext, hence reducing leakage chances. The suggested approach also incorporates an effective key management protocol and optimized processing algorithms to ensure performance while increasing security. Experimental assessment proves that HEPPF gains considerable enhancements in data privacy preservation without losing system efficiency. Results indicate decreased computational overhead, greater data breach resistance, and increased trust in IoT-edge structures. Therefore, the HEPPF framework efficiently solves current privacy issues and opens the door to more secure and scalable IoT implementations in real-world edge computing environments.