As mobile phone technology evolves quickly, people could use mobile phones to conduct business, watch entertainment shows, order food, and many more. These location-based services (LBS) require users’ mobility data (trajectories) in order to provide many useful services. Latent patterns and behavior that are hidden in trajectory data should be extracted and analyzed to improve location-based services including routing, recommendation, urban planning, traffic control, etc. While LBSs offer relevant information to mobile users based on their locations, revealing such areas can pose user privacy violation problems. An efficient privacy preservation algorithm for trajectory data must have two characteristics: utility and privacy, i.e., the anonymized trajectories must have sufficient utility for the LBSs to carry out their services, and privacy must be intact without any compromise. Literature on this topic shows many methods catering to trajectories based on GPS data. In this paper, we propose a privacy preserving method for trajectory data based on Call Detail Record (CDR) information. This is useful as a vast number of people, particularly in underdeveloped and developing places, either do not have GPS-enabled phones or do not use them. We propose a novel framework called Privacy-Preserving Trajectory Publication Framework for CDR (PPTPF) for moving object trajectories to address these concerns. Salient features of PPTPF include: (a) a novel stay-region based anonymization technique that caters to important locations of a user; (b) it is based on Spark, thus it can process and anonymize a significant volume of trajectory data successfully and efficiently without affecting LBSs operations; (c) it is a component-based architecture where each component can be easily extended and modified by different parties.

中文翻译：

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PPTPF：用于CDR移动轨迹的保护隐私的轨迹发布框架

随着手机技术的飞速发展，人们可以使用手机开展业务，观看娱乐节目，点菜等。这些基于位置的服务（LBS）需要用户的移动性数据（轨迹），以便提供许多有用的服务。应当提取和分析隐藏在轨迹数据中的潜在模式和行为，以改善基于位置的服务，包括路由，推荐，城市规划，交通控制等。LBS根据其位置向移动用户提供相关信息，从而揭示此类区域会造成用户隐私侵犯问题。有效的轨迹数据隐私保护算法必须具有两个特征：效用和隐私，即匿名轨迹必须具有足够的效用，以使LBS能够执行其服务；和隐私必须完整无损。关于该主题的文献显示了许多基于GPS数据来适应轨迹的方法。在本文中，我们提出了一种基于呼叫详细记录（CDR）信息的轨迹数据隐私保护方法。这是有用的，因为很多人（尤其是在欠发达和发展中的地方）没有启用GPS的电话或不使用它们。我们提出了一种称为CDR的隐私保护轨迹发布框架（PPTPF）的新颖框架，用于移动对象轨迹来解决这些问题。PPTPF的显着特征包括：（a）一种新颖的基于停留区的匿名化技术，可满足用户的重要位置；（b）它基于Spark，因此，它可以成功，有效地处理和匿名化大量轨迹数据，而不会影响LBS的运行；（c）这是一个基于组件的体系结构，其中每个组件都可以由不同的方轻松扩展和修改。