Location attestation and access control for mobile devices using GeoXACML

Access control has been applied in various scenarios in the past for negotiating the best policy. Solutions with XACML for access control has been very well explored by research and have resulted in significant contributions to various sectors including healthcare. In controlling access to the sensitive data such as medical records, it is important to guarantee that the data is accessed by the right person for the right reason. Location of access requestor can be a good indication for his/her eligibility and reasons for accessing the data. To reason with geospatial information for access control, Geospatial XACML (eXtensible Access Control Markup Language) is proposed as a standard. However, there is no available implementation and architecture for reasoning with Geospatial XACML policies. This paper proposes to extend XACML with geohashing to implement geospatial policies. It also proposes an architecture for checking reliability of the geospatial information provided by clients. With a case study, we demonstrate how our framework can be used to control the privacy and data access of health service data in handheld devices

LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection

The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection

THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE

Durant les quatre dernières décennies, la miniaturisation a permis la diffusion à large échelle des ordinateurs, les rendant omniprésents. Aujourd’hui, le nombre d’objets connectés à Internet ne cesse de croitre et cette tendance n’a pas l’air de ralentir. Ces objets, qui peuvent être des téléphones mobiles, des véhicules ou des senseurs, génèrent de très grands volumes de données qui sont presque toujours associés à un contexte spatiotemporel. Le volume de ces données est souvent si grand que leur traitement requiert la création de système distribués qui impliquent la coopération de plusieurs ordinateurs. La capacité de traiter ces données revêt une importance sociétale. Par exemple: les données collectées lors de trajets en voiture permettent aujourd’hui d’éviter les em-bouteillages ou de partager son véhicule. Un autre exemple: dans un avenir proche, les données collectées à l’aide de gyroscopes capables de détecter les trous dans la chaussée permettront de mieux planifier les interventions de maintenance à effectuer sur le réseau routier. Les domaines d’applications sont par conséquent nombreux, de même que les problèmes qui y sont associés. Les articles qui composent cette thèse traitent de systèmes qui partagent deux caractéristiques clés: un contexte spatiotemporel et une architecture décentralisée. De plus, les systèmes décrits dans ces articles s’articulent autours de trois axes temporels: le présent, le passé, et le futur. Les systèmes axés sur le présent permettent à un très grand nombre d’objets connectés de communiquer en fonction d’un contexte spatial avec des temps de réponses proche du temps réel. Nos contributions dans ce domaine permettent à ce type de système décentralisé de s’adapter au volume de donnée à traiter en s’étendant sur du matériel bon marché. Les systèmes axés sur le passé ont pour but de faciliter l’accès a de très grands volumes données spatiotemporelles collectées par des objets connectés. En d’autres termes, il s’agit d’indexer des trajectoires et d’exploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particulièrement denses, ce qui n’avait pas été fait auparavant. Enfin, les systèmes axés sur le futur utilisent les trajectoires passées pour prédire les trajectoires que des objets connectés suivront dans l’avenir. Nos contributions permettent de prédire les trajectoires suivies par des objets connectés avec une granularité jusque là inégalée. Bien qu’impliquant des domaines différents, ces contributions s’articulent autour de dénominateurs communs des systèmes sous-jacents, ouvrant la possibilité de pouvoir traiter ces problèmes avec plus de généricité dans un avenir proche. -- During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future

Autonomy and Efficiency Trade-offs on an Ethereum-Based Real Estate Application

Siiani on jagamismajanduse vahendusplatvorme arendatud tsentraliseeritud andmebaaside abil. Plokiahela esiletõus on aga ilmutanud uusi võimalusi, et muuta valdkonda võltsimiskindlaks ning vähendada vajadust vahendajate järele. Käesolevas töös uuritakse plokiahela kasutusvõimalusi kinnisvara rentimise protsessi näitel. Täpsemalt, töös disainitakse lahendus Ethereumi abil ning teostatakse kolm järjestikust prototüüpi, et analüüsida andmete ning arvutuste tõstmist plokiahelasse. Tulemused näitavad, et detsentraliseerimisel tuleb teha kompromisse teostatavuse ning tõhususe vahel.Marketplaces in sharing economy have traditionally been organized as web applications running on top of centralized databases. The advent of blockchain technology brings new opportunities, with the promise of transforming the landscape with tamper-resilient storage and the potential of reduction in intermediaries. In this context, in this thesis we look at exploring the use of blockchain technologies in the domain of real estate rental process. More specifically, we designed a solution on top of Ethereum and implemented three consecutive prototypes to analyze the impact of moving data and processing to the blockchain. The results show a trade-off between efficacy versus efficiency when moving toward decentralization

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity