The proliferation of smartphone devices has led to the emergence of powerful user services from enabling interactions with friends and business associates to mapping, finding nearby businesses and alerting users in real-time. Moreover, users do not realize that continuously sharing their trajectory data with online systems may end up revealing a great amount of information in terms of their behavior, mobility patterns and social relationships. Thus, addressing these privacy risks is a fundamental challenge. In this work, we present $TP^3$, a Privacy Protection system for Trajectory analytics. Our contributions are the following: (1) we model a new type of attack, namely 'social link exploitation attack', (2) we utilize the coresets theory, a fast and accurate technique which approximates well the original data using a small data set, and running queries on the coreset produces similar results to the original data, and (3) we employ the Serverless computing paradigm to accommodate a set of privacy operations for achieving high system performance with minimized provisioning costs, while preserving the users' privacy. We have developed these techniques in our $TP^3$ system that works with state-of-the-art trajectory analytics apps and applies different types of privacy operations. Our detailed experimental evaluation illustrates that our approach is both efficient and practical.

翻译：智能手机设备的普及催生了强大的用户服务，从支持与朋友和商业伙伴的互动，到地图导航、查找附近商家以及实时提醒用户。然而，用户并未意识到，持续与在线系统共享轨迹数据可能会暴露大量关于其行为、移动模式和社会关系的信息。因此，解决这些隐私风险是一项根本性挑战。在本工作中，我们提出$TP^3$，一个用于轨迹分析的隐私保护系统。我们的贡献如下：(1) 建模了一种新型攻击，即“社交链接利用攻击”；(2) 利用核心集理论，这是一种快速且精确的技术，能够用小数据集良好近似原始数据，并且在核心集上执行查询会产生与原始数据相似的结果；(3) 采用无服务器计算范式来容纳一组隐私操作，以在保护用户隐私的同时实现高系统性能和最小化配置成本。我们在$TP^3$系统中开发了这些技术，该系统可与最先进的轨迹分析应用协同工作，并应用不同类型的隐私操作。详细的实验评估表明，我们的方法既高效又实用。