Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Towards trajectory anonymization: A generalization-based approach

Trajectory datasets are becoming,popular,due,to the massive,usage,of GPS and,location- based services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity,to trajectories and propose,a novel generalization-based approach,for anonymization,of trajectories. We further show,that releasing anonymized,trajectories may,still have,some,privacy,leaks. Therefore we propose,a randomization based,reconstruction,algorithm,for releasing anonymized,trajectory data and,also present how,the underlying,techniques,can be adapted,to other anonymity,standards. The experimental,results on real and,synthetic trajectory datasets show,the effectiveness of the proposed,techniques

A Survey on Privacy in Human Mobility

In the last years we have witnessed a pervasive use of location-aware technologies such as vehicular GPS-enabled devices, RFID based tools, mobile phones, etc which generate collection and storing of a large amount of human mobility data. The powerful of this data has been recognized by both the scientific community and the industrial worlds. Human mobility data can be used for different scopes such as urban traffic management, urban planning, urban pollution estimation, etc. Unfortunately, data describing human mobility is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database and the access to the places visited by individuals may enable the inference of sensitive information such as religious belief, sexual preferences, health conditions, and so on. The literature reports many approaches aimed at overcoming privacy issues in mobility data, thus in this survey we discuss the advancements on privacy-preserving mobility data publishing. We first describe the adversarial attack and privacy models typically taken into consideration for mobility data, then we present frameworks for the privacy risk assessment and finally, we discuss three main categories of privacy-preserving strategies: methods based on anonymization of mobility data, methods based on the differential privacy models and methods which protect privacy by exploiting generative models for synthetic trajectory generation

A survey on privacy in human mobility

In the last years we have witnessed a pervasive use of location-aware technologies such as vehicular GPS-enabled devices, RFID based tools, mobile phones, etc which generate collection and storing of a large amount of human mobility data. The powerful of this data has been recognized by both the scientific community and the industrial worlds. Human mobility data can be used for different scopes such as urban traffic management, urban planning, urban pollution estimation, etc. Unfortunately, data describing human mobility is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database and the access to the places visited by indi-viduals may enable the inference of sensitive information such as religious belief, sexual preferences, health conditions, and so on. The literature reports many approaches aimed at overcoming privacy issues in mobility data, thus in this survey we discuss the advancements on privacy-preserving mo-bility data publishing. We first describe the adversarial attack and privacy models typically taken into consideration for mobility data, then we present frameworks for the privacy risk assessment and finally, we discuss three main categories of privacy-preserving strategies: methods based on anonymization of mobility data, methods based on the differential privacy models and methods which protect privacy by exploiting generative models for synthetic trajectory generation

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment