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:智能手机设备的扩散导致出现了强大的用户服务,从与朋友和商务伙伴的互动到实时绘图、寻找附近企业和提醒用户,从而促成与朋友和商务伙伴的互动,从而产生强大的用户服务。此外,用户没有认识到,与在线系统持续分享其轨迹数据可能最终暴露出大量有关其行为、流动性模式和社会关系的信息。因此,解决这些隐私风险是一个根本性挑战。在这项工作中,我们为轨迹分析提供了3美元,这是一个隐私保护系统。我们的贡献如下:(1) 我们模拟了一种新型攻击,即“社交链接剥削攻击”,(2) 我们使用了核心集理论,这是一种快速和准确的技术,它利用小数据集非常接近原始数据,在核心集上进行查询会产生与原始数据相似的结果。(3) 我们使用无服务器计算模式来适应一系列隐私操作,以尽可能降低供给成本的方式实现高的系统性能,同时保护用户的隐私。我们在我们的$TP3系统中开发了这些技术,它与最先进的轨迹分析应用程序一起工作,并应用了各种实用的隐私操作方法。