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

Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

Extending the Exposure Score of Web Browsers by Incorporating CVSS

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Yet its content differs from one browser to another. Despite the privacy and security risks of User-Agent strings, very few works have tackled this problem. Our previous work proposed giving Internet browsers exposure relative scores to aid users to choose less intrusive ones. Thus, the objective of this work is to extend our previous work through: first, conducting a user study to identify its limitations. Second, extending the exposure score via incorporating data from the NVD. Third, providing a full implementation, instead of a limited prototype. The proposed system: assigns scores to users’ browsers upon visiting our website. It also suggests alternative safe browsers, and finally it allows updating the back-end database with a click of a button. We applied our method to a data set of more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available here [4].</p

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Preserving Privacy in High-Dimensional Data Publishing

We are witnessing a continuous expansion of information technology that never ceases to impress us with its computational power, storage capacity, and agile mobility. Such technology is becoming more pervasive by the day and has enhanced various aspects of our daily lives. GPS-equipped devices, smart card automated fare collection systems, and sensory technology are but a few examples of advanced, yet affordable, data-generating technologies that are an integral part of modern society. To enhance user experience or provide better services, service providers rely on collecting person-specific information from users. Thus, the collected data is studied and analyzed in order to extract useful information. It is a common practice for the collected data to be shared with a third-party, e.g., a data mining firm, for data analysis. However, the shared data must not leak sensitive information about the individuals to whom the data belongs or reveal their identity. In other words, individuals’ privacy must be protected in the published data. Privacy-preserving data publishing is a research area that studies anonymizing person-specific data without compromising its utility for future data analysis. This thesis studies and proposes anonymization solutions for three types of high-dimensional data: trajectory streams, static trajectories, and relational data. We demonstrate through theoretical and experimental analysis that our proposed solutions, for the most part, outperform state-of-the-art methods in terms of utility, efficiency, and scalability

Push, Pull, and Spill: A Transdisciplinary Case Study in Municipal Open Government

Municipal open data raises hopes and concerns. The activities of cities produce a wide array of data, data that is vastly enriched by ubiquitous computing. Municipal data is opened as it is pushed to, pulled by, and spilled to the public through online portals, requests for public records, and releases by cities and their vendors, contractors, and partners. By opening data, cities hope to raise public trust and prompt innovation. Municipal data, however, is often about the people who live, work, and travel in the city. By opening data, cities raise concern for privacy and social justice. This article presents the results of a broad empirical exploration of municipal data release in the City of Seattle. In this research, parties affected by municipal practices expressed their hopes and concerns for open data. City personnel from eight prominent departments described the reasoning, procedures, and controversies that have accompanied their release of data. All of the existing data from the online portal for the city were joined to Municipal open data raises hopes and concerns. The activities of cities produce a wide array of data, data that is vastly enriched by ubiquitous computing. Municipal data is opened as it is pushed to, pulled by, and spilled to the public through online portals, requests for public records, and releases by cities and their vendors, contractors, and partners. By opening data, cities hope to raise public trust and prompt innovation. Municipal data, however, is often about the people who live, work, and travel in the city. By opening data, cities raise concern for privacy and social justice. Results suggest the need for more comprehensive measures to manage the risk latent in opening city data. Cities should maintain inventories of data assets, produce data management plans pertaining to the activities of departments, and develop governance structures to deal with issues as they arise—centrally and amongst the various departments—with ex ante and ex post protocols to govern the push, pull, and spill of data. In addition, cities should consider conditioned access to pushed data, conduct audits and training around public records requests, and develop standardized model contracts to protect against the spill of data by third parties

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Trajectory Privacy Preservation and Lightweight Blockchain Techniques for Mobility-Centric IoT

Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity on resource-constrained devices and can only provide conditional privacy when a set of authorities governs the blockchain. This dissertation addresses these challenges to develop efficient trajectory privacy-preservation and lightweight blockchain techniques for mobility-centric IoT. We develop a pruning-based technique by quantifying the relationship between trajectory privacy and delay for real-time geo-tagged queries. This technique yields higher trajectory privacy with a reduced delay than contemporary techniques while preventing a long-term observation attack. We extend our study with the consideration of the presence of non-geo-tagged data in a trajectory. We design an attack model to show the spatiotemporal correlation between the geo-tagged and non-geo-tagged data which undermines the privacy guarantee of existing techniques. In response, we propose a methodology that considers the spatial distribution of the data in trajectory privacy-preservation and improves existing solutions, in privacy and usability. With respect to blockchain, we design and implement one of the first blockchain storage management techniques utilizing the mobility of the devices. This technique reduces the required storage space of a blockchain and makes it lightweight for resource-constrained mobile devices. To address the trajectory privacy challenges in an authority-based blockchain under the short-range communication constraints of the devices, we introduce a silence-based one of the first technique to establish a balance between trajectory privacy and blockchain utility. The designed trajectory privacy- preservation techniques we established are light- weight and do not require an intermediary to guarantee trajectory privacy, thereby providing practical and efficient solution for different mobility-centric IoT, such as mobile crowdsensing and Internet of Vehicles