Research on Encryption and Decryption Technology of Microservice Communication Based on Block Cipher
The efficiency optimization of encryption and decryption algorithms in cloud environments is addressed in this study, where the processing speed of encryption and decryption is enhanced through the application of multi-threaded parallel technology. In view of the high-concurrency and distributed storage characteristics of cloud platforms, a multi-threaded concurrency mechanism is adopted for the direct processing of data streams. Compared with the traditional serial processing mode, four distinct encryption algorithms, namely AES, DES, SM4 and Ascon, are employed, and different data units are processed concurrently by means of multithreaded technology. Based on multi-dimensional performance evaluation indicators (including throughput, memory footprint and security level), comparative analyses are carried out to optimize the design scheme; accordingly, multi-threaded collaborative encryption is realized to improve the overall operation efficiency. Experimental results indicate that, in comparison with the traditional serial encryption method, the encryption and decryption latency of the algorithm is reduced by around 50%, which significantly lowers the time overhead associated with encryption and decryption processes. Simultaneously, the throughput of AES and DES algorithms is observed to be doubled, which leads to a remarkable improvement in communication efficiency. Moreover, under the premise that the original secure communication capability is guaranteed, system resource overhead is effectively reduced by SM4 and Ascon algorithms. On this basis, a quantitative reference basis is provided for cloud platforms to develop targeted encryption strategies tailored to diverse business demands. In conclusion, the proposed approach is of profound significance for advancing the synergistic optimization of security and performance in cloud-native data communication scenarios.
Authors
- Shijie Zhang (ORCID: https://orcid.org/0000-0001-8489-4010)
- Ting Fan
- Xiaolan Xie
- Yu Wang
Institutions
- Guilin University of Technology (CN)
Publication Details
- Journal
- Electronics
- Published
- 2026-01-19
- DOI
- https://doi.org/10.3390/electronics15020431
- Primary Topic
- Big Data and Digital Economy
- Type
- article
- Field-Weighted Citation Impact
- 0.00