Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Preserving Users’ Location Privacy in Mobile Platforms

Mobile and interconnected devices both have witnessed rapid advancements in computing and networking capabilities due to the emergence of Internet-of-Things, Connected Societies, Smart Cities and other similar paradigms. Compared to traditional personal computers, these devices represent moving gateways that offer possibilities to influence new businesses and, at the same time, have the potential to exchange users’ sensitive data. As a result, this raises substantial threats to the security and privacy of users that must be considered. With the focus on location data, this thesis proposes an efficient and socially-acceptable solution to preserve users’ location privacy, maintaining the quality of service, and respecting the usability by not relying on changes to the mobile app ecosystem. This thesis first analyses the current mobile app ecosystem as to apply a privacy-bydesign approach to location privacy from the data computation to its visualisation. From our analysis, a 3-Layer Classification model is proposed that depicts the state-ofthe- art in three layers providing a new perspective towards privacy-preserving locationbased applications. Secondly, we propose a theoretically sound privacy-enhancing model, called LP-Cache, that forces the mobile app ecosystem to make location data usage patterns explicit and maintains the balance between location privacy and service utility. LP-Cache defines two location privacy preserving algorithms: on-device location calculation and personalised permissions. The former incorporates caching technique to determine the location of client devices by means of wireless access points and achieve data minimisation in the current process. With the later, users can manage each app and private place distinctly to mitigate fundamental location privacy threats, such as tracking, profiling, and identification. Finally, PL-Protector, implements LP-Cache as a middleware on Android platform. We evaluate PL-Protector in terms of performance, privacy, and security. Experimental results demonstrate acceptable delay and storage overheads, which are within practical limits. Hence, we claim that our approach is a practical, secure and efficient solution to preserve location privacy in the current mobile app ecosystem

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

Location Privacy Protection in the Mobile Era and Beyond

As interconnected devices become embedded in every aspect of our lives, they accompany many privacy risks. Location privacy is one notable case, consistently recording an individual’s location might lead to his/her tracking, fingerprinting and profiling. An individual’s location privacy can be compromised when tracked by smartphone apps, in indoor spaces, and/or through Internet of Things (IoT) devices. Recent surveys have indicated that users genuinely value their location privacy and would like to exercise control over who collects and processes their location data. They, however, lack the effective and practical tools to protect their location privacy. An effective location privacy protection mechanism requires real understanding of the underlying threats, and a practical one requires as little changes to the existing ecosystems as possible while ensuring psychological acceptability to the users. This thesis addresses this problem by proposing a suite of effective and practical privacy preserving mechanisms that address different aspects of real-world location privacy threats. First, we present LP-Guardian, a comprehensive framework for location privacy protection for Android smartphone users. LP-Guardian overcomes the shortcomings of existing approaches by addressing the tracking, profiling, and fingerprinting threats posed by different mobile apps while maintaining their functionality. LP-Guardian requires modifying the underlying platform of the mobile operating system, but no changes in either the apps or service provider. We then propose LP-Doctor, a light-weight user-level tool which allows Android users to effectively utilize the OS’s location access controls. As opposed to LP-Guardian, LP-Doctor requires no platform changes. It builds on a two year data collection campaign in which we analyzed the location privacy threats posed by 1160 apps for 100 users. For the case of indoor location tracking, we present PR-LBS (Privacy vs. Reward for Location-Based Service), a system that balances the users’ privacy concerns and the benefits of sharing location data in indoor location tracking environments. PR-LBS fits within the existing indoor localization ecosystem whether it is infrastructure-based or device-based. Finally, we target the privacy threats originating from the IoT devices that employ the emerging Bluetooth Low Energy (BLE) protocol through BLE-Guardian. BLE-Guardian is a device agnostic system that prevents user tracking and profiling while securing access to his/her BLE-powered devices. We evaluate BLE-Guardian in real-world scenarios and demonstrate its effectiveness in protecting the user along with its low overhead on the user’s devices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138563/1/kmfawaz_1.pd

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Quantifying Privacy Loss of Human Mobility Graph Topology

Abstract Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy.</jats:p