Privacy-preserving Publication of Mobility Data with High Utility

An increasing amount of mobility data is being collected every day by different means, e.g., by mobile phone operators. This data is sometimes published after the application of simple anonymization techniques, which might lead to severe privacy threats. We propose in this paper a new solution whose novelty is twofold. Firstly, we introduce an algorithm designed to hide places where a user stops during her journey (namely points of interest), by enforcing a constant speed along her trajectory. Secondly, we leverage places where users meet to take a chance to swap their trajectories and therefore confuse an attacker.Comment: 2015 35th IEEE International Conference on Distributed Computed System

Privacy in trajectory micro-data publishing : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac

PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

Exploring movement – similarity analysis of moving objects

Extracting knowledge about the movement of different types of mobile agents (e.g. human, animals, vehicles) and dynamic phenomena (e.g. hurricanes) requires new exploratory data analysis methods for massive movement datasets. Different types of moving objects share similarities but also express differences in terms of their dynamic behavior and the nature of their movement. Extracting such similarities can significantly contribute to the prediction, modeling and simulation dynamic phenomena. Therefore, with the development of a quantitative methodology this research intends to investigate and explore similarities in the dynamics of moving objects by using methods of GIScience in knowledge discovery. This paper presents a summary of the ongoing Ph.D. research project

Privacy preserving distributed spatio-temporal data mining

Time-stamped location information is regarded as spatio-temporal data due to its time and space dimensions and, by its nature, is highly vulnerable to misuse. Privacy issues related to collection, use and distribution of individuals’ location information are the main obstacles impeding knowledge discovery in spatio-temporal data. Suppressing identifiers from the data does not suffice since movement trajectories can easily be linked to individuals using publicly available information such as home or work addresses. Yet another solution could be employing existing privacy preserving data mining techniques. However these techniques are not suitable since time-stamped location observations of an object are not plain, independent attributes of this object. Therefore, new privacy preserving data mining techniques are required to handle spatio-temporal data specifically. In this thesis, we propose a privacy preserving data mining technique and two preprocessing steps for data mining related to privacy preservation in spatio-temporal datasets: (1) Distributed clustering, (2) Centralized anonymization and (3) Distributed anonymization. We also provide security and efficiency analysis of our algorithms which shows that under reasonable conditions, achieving privacy preservation with minimal sensitive information leakage is possible for data mining purposes