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、查找附近商户以及实时提醒用户。然而，用户并未意识到，持续向在线系统分享其轨迹数据可能会泄露大量关于其行为、移动模式及社交关系的信息。因此，应对这些隐私风险是一项根本性挑战。本研究提出了$TP^3$——一种面向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（coresets theory），这是一种快速且精确的技术，通过使用小数据集良好近似原始数据，在核心集上执行查询可获得与原始数据相似的结果；(3) 采用无服务器计算范式（Serverless computing paradigm）来实施一系列隐私操作，在保障用户隐私的同时实现高系统性能并最小化资源调配成本。我们将这些技术整合到$TP^3$系统中，该系统可与最先进的轨迹分析应用协作，并应用不同类型的隐私操作。详细的实验评估表明，我们的方法兼具高效性与实用性。