Improvements of Homomorphic Encryption Algorithms and Their Effects in Privacy Protection in the Internet of Things

At present, traditional homomorphic encryption (HE) algorithms face the problems of high computational consumption and weak antiattack ability in complex environments. In order to handle these shortcomings and improve the HE algorithm, the ring learning with errors (RLWE) is proposed. The results show that the proposed algorithm can effectively reduce the computational overhead by combining local differential privacy mechanisms with Nth‐degree truncated polynomial ring units (NTRU) lattice‐based lightweight encryption schemes. Additionally, data security can be made better. On the Espressif Systems Protocol 32‐bit (ESP32) platform, encryption latency is reduced by 42.1% compared to the Cheon–Kim–Kim–Song (CKKS) algorithm. With the incorporation of an adaptive mechanism, the attack rate of breaking through the defense line is reduced to below 12%. Meanwhile, this algorithm can also guarantee the quantum‐resistant security strength of 148 bits in the configuration of 2048‐bit security parameters; it comprehensively surpasses CKKS, RLWE‐CKKS, and many improved baseline algorithms. Under the harsh conditions of the Internet of Things (IoT) terminal, this study simultaneously tackles computational constraints, privacy leakage risks, and postquantum threats; thus, it can offer a theoretical reference and engineering support for building a trustworthy IoT system.