Many Intelligent Transportation Systems (ITS) applications require strong privacy guarantees for both users and their data. Homomorphic encryption (HE) enables computation directly on encrypted messages and thus offers a compelling approach to privacy-preserving data processing in ITS. However, practical HE schemes incur substantial ciphertext expansion and communication overhead, which limits their suitability for time-critical transportation systems. Hybrid homomorphic encryption (HHE) addresses this challenge by combining a homomorphic encryption scheme with a symmetric cipher, enabling efficient encrypted computation while dramatically reducing communication cost. In this paper, we develop theoretical models of representative ITS applications that integrate HHE to protect sensitive vehicular data. We then perform a parameter-based evaluation of the HHE scheme Rubato to estimate ciphertext sizes and communication overhead under realistic ITS workloads. Our results show that HHE achieves orders-of-magnitude reductions in ciphertext size compared with conventional HE while maintaining cryptographic security, making it significantly more practical for latency-constrained ITS communication.

翻译：许多智能交通系统（ITS）应用需要对用户及其数据提供严格的隐私保障。同态加密（HE）能够直接在加密消息上进行计算，从而为ITS中的隐私保护数据处理提供了一种极具吸引力的方法。然而，实用的同态加密方案会产生显著的密文膨胀和通信开销，这限制了其在时间敏感的交通系统中的适用性。混合同态加密（HHE）通过将同态加密方案与对称密码相结合来解决这一挑战，在实现高效加密计算的同时大幅降低通信成本。本文针对集成HHE以保护敏感车辆数据的典型ITS应用建立了理论模型。随后，我们对HHE方案Rubato进行了基于参数的评估，以估算实际ITS工作负载下的密文大小和通信开销。结果表明，与传统HE相比，HHE在保持密码学安全性的同时实现了密文大小的数量级缩减，使其在延迟受限的ITS通信中具有显著更高的实用性。