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

THE INFLUENCE OF URBAN FORM AT DIFFERENT GEOGRAPHICAL SCALES ON TRAVEL BEHAVIOR; EVIDENCE FROM U.S. CITIES

Suburban lifestyle is popular among American families, although it has been criticized for encouraging automobile use through longer commutes, causing heavy traffic congestion, and destroying open spaces (Handy, 2005). It is a serious concern that people living in low-density suburban areas suffer from high automobile dependency and lower rates of daily physical activity, both of which result in social, environmental and health-related costs. In response to such concerns, researchers have investigated the inter-relationships between urban land-use pattern and travel behavior within the last few decades and suggested that land-use planning can play a significant role in changing travel behavior in the long-term. However, debates regarding the magnitude and efficiency of the effects of land-use on travel patterns have been contentious over the years. Changes in built-environment patterns is potentially considered a long-term panacea for automobile dependency and traffic congestion, despite some researchers arguing that the effects of land-use on travel behavior are minor, if any. It is still not clear why the estimated impact is different in urban areas and how effective a proposed land-use change/policy is in changing certain travel behavior. This knowledge gap has made it difficult for decision-makers to evaluate land-use plans and policies. In addition, little is known about the influence of the large-scale built environment. In the present dissertation, advanced spatial-statistical tools have been employed to better understand and analyze these impacts at different scales, along with analyzing transit-oriented development policy at both small and large scales. The objective of this research is to: (1) develop scalable and consistent measures of the overall physical form of metropolitan areas; (2) re-examine the effects of built-environment factors at different hierarchical scales on travel behavior, and, in particular, on vehicle miles traveled (VMT) and car ownership; and (3) investigate the effects of transit-oriented development on travel behavior. The findings show that changes in built-environment at both local and regional levels could be very influential in changing travel behavior. Specifically, the promotion of compact, mixed-use built environment with well-connected street networks reduces VMT and car ownership, resulting in less traffic congestion, air pollution, and energy consumption

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

Towards Intelligent Distribution Systems: Solutions for Congestion Forecast and Dynamic State Estimation Based Protection

The electrical distribution systems are undergoing drastic changes such as increasing penetration level of distributed renewable energy sources, energy storage, electrification of energy-efficient loads such as heat pumps and electric vehicles, etc., since the last decade, and more changes are expected in the future. These changes pose challenges for the distribution system operators such as increased level of network congestions, voltage variations, as well as protection settings and coordination, etc. These will require the development of new paradigms to operate distribution systems securely, safely, and economically while hosting a large amount of renewable energy sources.First, the thesis proposed a comprehensive assessment framework to assess the distribution system operator’s future-readiness and support them in determining the current status of their network infrastructures, business models, and policies and thus to identify areas for required developments. The analysis for the future-readiness of the three distribution system operators (from France, The Netherlands, and Sweden) using the proposed assessment framework has shown that presently the distribution system operators have a rather small penetration of renewable energy sources in their grids, however, which is expected to increase in the future. The distribution system operators would need investments in flexibilities, novel forecasting techniques, advanced grid control as well as improved protection schemes. The need for the development of new business models for customers and changes in the policy and regulations are also suggested by the analysis. Second, the thesis developed a congestion forecast tool that would support the distribution system operators to forecast and visualize network overloading and voltage variations issues for multiple forecasting horizons ranging from close-to-real time to day-ahead. The tool is based on a probabilistic power flow that incorporates forecasts of production from solar photovoltaic and electricity demand combined with load models along with the consideration of different operating modes of solar photovoltaic inverters to enhance the accuracy. The congestion forecast tool can be integrated into the existing distribution management systems of distribution system operators via an open cross-platform using Codex Smart Edge technology of Atos Worldgrid. The congestion forecast tool has been used in a case study for two real distribution systems (7-bus feeder and 141-bus system). It was demonstrated in the case study that the tool can predict the congestion in the networks with various prediction horizons. The congestion forecast tool would support distribution system operators by forecasting the network congestion and setting up a congestion management plan.Finally, the dynamic state estimation based protection scheme supported by advanced measurement technologies developed within EU project UNITED-GRID has been implemented and validated experimentally at Chalmers power system laboratory. This dynamic state estimation based protection scheme has a strong advantage over the traditional protection scheme as it does not require any relay settings and coordination which can overcome the protection challenges arising in distribution grids with a large amount of renewable energy sources. The results from the validation of the dynamic state estimation based protection scheme at Chalmers laboratory have shown that the fault detection using this scheme has worked properly as expected for an application of the line protection

Development and Applications of Similarity Measures for Spatial-Temporal Event and Setting Sequences

Similarity or distance measures between data objects are applied frequently in many fields or domains such as geography, environmental science, biology, economics, computer science, linguistics, logic, business analytics, and statistics, among others. One area where similarity measures are particularly important is in the analysis of spatiotemporal event sequences and associated environs or settings. This dissertation focuses on developing a framework of modeling, representation, and new similarity measure construction for sequences of spatiotemporal events and corresponding settings, which can be applied to different event data types and used in different areas of data science. The first core part of this dissertation presents a matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This framework supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the modified Jaccard index with temporal order constraints and accommodates different event data types. This approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. These similarity measures are incorporated into a clustering method and successfully demonstrate the usefulness in a case study analysis of event sequences extracted from space time series of a water quality monitoring system. This dissertation further proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been considered. While modeling event setting sequences, spatial and temporal scales are considered to define the bounds of the setting and incorporate dynamic variables along with static variables. Using a matrix-based representation and an extended Jaccard index, new similarity measures are developed to allow for the use of all variable data types. With these similarity measures coupled with other multivariate statistical analysis approaches, results from a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, support the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. To test the scalability of STES similarity measure in a larger dataset and an extended application in different fields, this dissertation compares and contrasts the prospective space-time scan statistic with the STES similarity approach for identifying COVID-19 hotspots. The COVID-19 pandemic has highlighted the importance of detecting hotspots or clusters of COVID-19 to provide decision makers at various levels with better information for managing distribution of human and technical resources as the outbreak in the USA continues to grow. The prospective space-time scan statistic has been used to help identify emerging disease clusters yet results from this approach can encounter strategic limitations imposed by the spatial constraints of the scanning window. The STES-based approach adapted for this pandemic context computes the similarity of evolving normalized COVID-19 daily cases by county and clusters these to identify counties with similarly evolving COVID-19 case histories. This dissertation analyzes the spread of COVID-19 within the continental US through four periods beginning from late January 2020 using the COVID-19 datasets maintained by John Hopkins University, Center for Systems Science and Engineering (CSSE). Results of the two approaches can complement with each other and taken together can aid in tracking the progression of the pandemic. Overall, the dissertation highlights the importance of developing similarity measures for analyzing spatiotemporal event sequences and associated settings, which can be applied to different event data types and used for data mining, sequence classification, and clustering

LINKING ENVIRONMENTAL EXPOSURES AND HEALTH EFFECTS: USING EXISTING DATA TO EXPLORE THE RELATIONSHIPS BETWEEN ENVIRONMENT AND CHRONIC DISEASES

The environment plays an important role in the health of communities. However, few health systems exist at the state and/or local levels to efficiently track the potential health effects associated with environmental exposure. The objectives of this dissertation are 1) to use secondary data for assessing the possible associations between health outcomes and environmental exposure and/or hazard; 2) to explore possible methods of data linkage and analyses which can be used by state and local environmental health tracking agencies and 3) to bring positive contributions to the development of national Environmental Public Health Tracking Network (EPHT). In this project, the Three Mile Island (TMI) cohort data (1979-1995) and Pennsylvania (PA) Cancer registry data were used to evaluate the associations between cigarette smoking and adult leukemia. A case-crossover analysis was performed with PA cardiopulmonary hospital admission data and local air pollution data to assess the health effects of air pollutants on cardiopulmonary disease before and after the elimination of a major point source of air pollution. A case-control study was also conducted to examine the associations between term low birth weight and particulate air pollution. The results showed that cigarette smoking could increase the risk of acute myeloid leukemia (AML). In addition, particulate air pollution is significantly associated with cardiovascular hospitalization and low birth weight in term infant. In conclusion, the findings suggest that environmental hazards have adverse health effects on a number of health endpoints. Secondary data can be a great resource for environmental public health tracking, which is of public health relevance. The use of existing data is an effective way to assess the potential health effects associated with environmental exposures after an appropriate study design with a feasible data linkage and correct methods of data analyses was developed