Smart mobility is a promising approach to meet urban transport needs in an environmentally and and user-friendly way. Smart mobility computes itineraries with multiple means of transportation, e.g., trams, rental bikes or electric scooters, according to customer preferences. A mobility platform cares for reservations, connecting transports, invoicing and billing. This requires sharing sensible personal data with multiple parties, and puts data privacy at risk. In this paper, we investigate if fully homomorphic encryption (FHE) can be applied in practice to mitigate such privacy issues. FHE allows to calculate on encrypted data, without having to decrypt it first. We implemented three typical distributed computations in a smart mobility scenario with SEAL, a recent programming library for FHE. With this implementation, we have measured memory consumption and execution times for three variants of distributed transactions, that are representative for a wide range of smart mobility tasks. Our evaluation shows, that FHE is indeed applicable to smart mobility: With today's processing capabilities, state-of-the-art FHE increases a smart mobility transaction by about 100 milliseconds and less than 3 microcents.

翻译：智能出行是一种以环境友好和用户友好的方式满足城市交通需求的前景广阔的方法。智能出行根据客户偏好，计算包含多种交通方式（例如有轨电车、租赁自行车或电动滑板车）的行程方案。出行平台负责预订、交通衔接、开票和计费。这需要与多方共享敏感的个人数据，从而带来数据隐私风险。在本文中，我们研究全同态加密（FHE）是否能在实践中应用于缓解此类隐私问题。FHE允许对加密数据进行计算，而无需先解密。我们使用最新的FHE编程库SEAL，在智能出行场景中实现了三种典型的分布式计算。通过该实现，我们测量了三种具有代表性的分布式事务变体的内存消耗和执行时间，这些变体覆盖了广泛的智能出行任务。我们的评估表明，FHE确实适用于智能出行：在当今的处理能力下，最先进的FHE使智能出行事务增加约100毫秒和不到3微美分的成本。