On the Validity of Geosocial Mobility Traces

Mobile networking researchers have long searched for largescale, fine-grained traces of human movement, which have remained elusive for both privacy and logistical reasons. Recently, researchers have begun to focus on geosocial mobility traces, e.g. Foursquare checkin traces, because of their availability and scale. But are we conceding correctness in our zeal for data? In this paper, we take initial steps towards quantifying the value of geosocial datasets using a large ground truth dataset gathered from a user study. By comparing GPS traces against Foursquare checkins, we find that a large portion of visited locations is missing from checkins, and most checkin events are either forged or superfluous events. We characterize extraneous checkins, describe possible techniques for their detection, and show that both extraneous and missing checkins introduce significant errors into applications driven by these traces

Personal activity centres and geosocial data analysis: Combining big data with small data

Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city

Improving Location Accuracy And Network Capacity In Mobile Networks

Todays mobile computing must support a wide variety of applications such as location-based services, navigation, HD media streaming and augmented reality. Providing such services requires large network bandwidth and precise localization mechanisms, which face significant challenges. First, new (real-time) localization mechanisms are needed to locate neighboring devices/objects with high accuracy under tight environment constraints, e.g. without infrastructure support. Second, mobile networks need to deliver orders of magnitude more bandwidth to support the exponentially increasing traffic demand, and adapt resource usage to user mobility.In this dissertation, we build effective and practical solutions to address these challenges. Our first research area is to develop new localization mechanisms that utilize the rich set of sensors on smartphones to implement accurate localization systems. We propose two designs. The first system tracks distance to nearby devices with centimeter accuracy by transmitting acoustic signals between the devices. We design robust and efficient signal processing algorithms that measure distances accurately on the fly, thus enabling real-time user motion tracking. Our second system locates a transmitting device in real-time using commodity smart- phones. Driving by the insight that rotating a wireless receiver (smartphone) around a users body can effectively emulate the sensitivity and functionality of a directional antenna, we design a rotation-based measurement algorithm that can accurately predict the direction of the target transmitter and locate the transmitter with a few measurements.Our second research area is to develop next generation mobile networks to significantly boost network capacity. We propose a drastically new outdoor picocell design that leverages millimeter wave 60GHz transmissions to provide multi-Gbps bandwidth for mobile users. Using extensive measurements on off-the-shelf 60GHz radios, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths on 60GHz, and show that 60GHz outdoor picocells are indeed a feasible approach for delivering orders of magnitude increase in network capacity.Finally, we seek to capture and understand user mobility patterns which are essential in mobile network design and deployment. While traditional methods of collecting human mobility traces are expensive and not scalable, we explore a new direction that extracts large-scale mobility traces through widely available geosocial datasets, e.g. Foursquare "check-in" datasets. By comparing raw GPS traces against Foursquare checkins, we analyze the value of using geosocial datasets as representative traces of human mobility. We then develop techniques to both "sanitize" and "repopulate" geosocial traces, thus producing detailed mobility traces more indicative of actual human movement and suitable for mobile network design

Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks

Vehicular adhoc networks allow vehicles to share their information for safety and traffic efficiency. However, sharing information may threaten the driver privacy because it includes spatiotemporal information and is broadcast publicly and periodically. In this paper, we propose a context-adaptive pseudonym changing scheme which lets a vehicle decide autonomously when to change its pseudonym and how long it should remain silent to ensure unlinkability. This scheme adapts dynamically based on the density of the surrounding traffic and the user privacy preferences. We employ a multi-target tracking algorithm to measure privacy in terms of traceability in realistic vehicle traces. We use Monte Carlo analysis to estimate the quality of service (QoS) of a forward collision warning application when vehicles apply this scheme. According to the experimental results, the proposed scheme provides a better compromise between traceability and QoS than a random silent period scheme.Comment: Extended version of a previous paper "K. Emara, W. Woerndl, and J. Schlichter, "Poster: Context-Adaptive User-Centric Privacy Scheme for VANET," in Proceedings of the 11th EAI International Conference on Security and Privacy in Communication Networks, SecureComm'15. Dallas, TX, USA: Springer, June 2015.

Social Space and Social Media: Analyzing Urban Space with Big Data

This dissertation focuses on the key role that big data can play in minimizing the perceived disconnect between social theory and quantitative methods in the discipline of geography. It takes as its starting point the geographic concept of space, which is conceptualized very differently in social theory versus quantitative methodology. Contrary to this disparity, an examination of the disciplinary history reveals a number of historic precedents and potential pathways for a rapprochement, especially when combined with some of the new possibilities of big data. This dissertation also proposes solutions to two common barriers to the adoption of big data in the social sciences: accessing and collecting such data and, subsequently, meaningful analysis. These methods and the theoretical foundation are combined in three case studies that show the successful integration of a quantitative research methodology with social theories on space. The case studies demonstrate how such an approach can create new and alternative understandings of urban space. In doing so it answers three specific research questions: (1) How can big data facilitate the integration of social theory on space with quantitative research methodology? (2) What are the practical challenges and solutions to moving “beyond the geotag” when utilizing big data in geographical research? (3) How can the quantitative analysis of big data provide new and useful insight in the complex character of social space? More specifically, what insights does such an analysis of relational social space provide about urban mobility and cognitive neighborhoods

Geocaching: tracing geotagged social media research using mixed methods

This thesis explores the development of academic research with geotagged social media data (geosocial research) - an emerging computational, digital social research field - using 19 semi-structured interviews with scholars from diverse disciplines, participant observation at a geosocial research summer school and scientometrics. It asks: 'how can we study the development of geosocial research approaches through combining STS and scientometrics?' for five main reasons: to explore the diversity of computational social research; reflect on the ESRC's (2013) call to 'close the gap' between quantitative and qualitative human geography; contribute to methodological discussions in academic literature which call for combining STS and scientometrics; co-compose knowledge with distinct ways of knowing through mixing methods; and inform research methods curriculum development in the social sciences. Using new forms of digital data (like social media posts) is core to contemporary social science. Scholars from diverse disciplines conduct geosocial research. It thus provides rich opportunities to study how diverse approaches to computational social research develop. I combine STS and diverse scientometric methods as part of a single case study iteratively to explore how they can co-compose knowledge. The thesis contributes to literature which explores the STS - scientometrics interface. Most existing studies either reflect on diverse mixed methods approaches from theoretical or methodological perspectives, or provide worked examples using specific mixed methods designs. Conceptually, this thesis contributes by highlighting the need to develop and evaluate the affordances of computational methods for STS in light of the interpretative context - including research questions, characteristics of the studied research practice, theories and prior findings. I developed computational methods iteratively, in light of my theoretical and empirical knowledge about geosocial research. Empirically, the thesis first contributes by showing how diverse combinations of STS and scientometrics – including statistical and visual network analyses as well as descriptive statistics - can inform a single case study. Second, it offers three ways STS and scientometrics can co-compose knowledge by aligning their units of analyses, reflecting on how calculation acts inform qualitative analysis even when analytical units are not aligned, and using each method inductively. I combined STS and scientometrics to study practices through which geosocial research approaches develop - including collaboration, developing (sub)-disciplinary communities and methods' mediation of geosocial research. I also identified geosocial research approaches and compared them using mixed methods. Finally, I combined insights from STS and scientometrics to highlight the construction of my own analyses. Using mixed methods, the thesis argues that geosocial research is a collection of approaches rather than a coordinated community. I highlight fourteen practices that enable scholars to develop their approaches, including interdisciplinary collaboration; setting up distinct geosocial laboratories to experiment with geosocial data; reflecting on the data analysis process; and using local knowledge about spaces. I differentiate `social', `technical' and 'geographic' approaches, which differ in terms of the methods they use and spatial units they study. Finally, I illustrate approaches' heterogeneity - including their diverse computational approaches - and similarities, such as their urban studies focus

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

Geocaching: tracing geotagged social media research using mixed methods

This thesis explores the development of academic research with geotagged social media data (geosocial research) - an emerging computational, digital social research field - using 19 semi-structured interviews with scholars from diverse disciplines, participant observation at a geosocial research summer school and scientometrics. It asks: 'how can we study the development of geosocial research approaches through combining STS and scientometrics?' for five main reasons: to explore the diversity of computational social research; reflect on the ESRC's (2013) call to 'close the gap' between quantitative and qualitative human geography; contribute to methodological discussions in academic literature which call for combining STS and scientometrics; co-compose knowledge with distinct ways of knowing through mixing methods; and inform research methods curriculum development in the social sciences. Using new forms of digital data (like social media posts) is core to contemporary social science. Scholars from diverse disciplines conduct geosocial research. It thus provides rich opportunities to study how diverse approaches to computational social research develop. I combine STS and diverse scientometric methods as part of a single case study iteratively to explore how they can co-compose knowledge. The thesis contributes to literature which explores the STS - scientometrics interface. Most existing studies either reflect on diverse mixed methods approaches from theoretical or methodological perspectives, or provide worked examples using specific mixed methods designs. Conceptually, this thesis contributes by highlighting the need to develop and evaluate the affordances of computational methods for STS in light of the interpretative context - including research questions, characteristics of the studied research practice, theories and prior findings. I developed computational methods iteratively, in light of my theoretical and empirical knowledge about geosocial research. Empirically, the thesis first contributes by showing how diverse combinations of STS and scientometrics – including statistical and visual network analyses as well as descriptive statistics - can inform a single case study. Second, it offers three ways STS and scientometrics can co-compose knowledge by aligning their units of analyses, reflecting on how calculation acts inform qualitative analysis even when analytical units are not aligned, and using each method inductively. I combined STS and scientometrics to study practices through which geosocial research approaches develop - including collaboration, developing (sub)-disciplinary communities and methods' mediation of geosocial research. I also identified geosocial research approaches and compared them using mixed methods. Finally, I combined insights from STS and scientometrics to highlight the construction of my own analyses. Using mixed methods, the thesis argues that geosocial research is a collection of approaches rather than a coordinated community. I highlight fourteen practices that enable scholars to develop their approaches, including interdisciplinary collaboration; setting up distinct geosocial laboratories to experiment with geosocial data; reflecting on the data analysis process; and using local knowledge about spaces. I differentiate `social', `technical' and 'geographic' approaches, which differ in terms of the methods they use and spatial units they study. Finally, I illustrate approaches' heterogeneity - including their diverse computational approaches - and similarities, such as their urban studies focus

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming