Smart transportation systems rely on large-scale data processing and real-time communication to improve traffic management and public safety. This paper investigates the use of cloud computing in intelligent transportation applications such as traffic pre

Abstract

Data privacy is one of the most significant challenges in cloud computing environments due to shared infrastructure and remote data storage. This paper examines privacy preservation techniques including homomorphic encryption, access control models, anonymization, and secure multi-party computation. The proposed framework ensures secure data processing and confidentiality while maintaining cloud service performance. Comparative analysis demonstrates that advanced privacy-preserving mechanisms can effectively reduce data leakage risks and unauthorized access. The paper concludes that strong privacy protection strategies are essential for increasing trust in cloud services.