Gestion efficace et partage sécurisé des traces de mobilité

Nowadays, the advances in the development of mobile devices, as well as embedded sensors have permitted an unprecedented number of services to the user. At the same time, most mobile devices generate, store and communicate a large amount of personal information continuously. While managing personal information on the mobile devices is still a big challenge, sharing and accessing these information in a safe and secure way is always an open and hot topic. Personal mobile devices may have various form factors such as mobile phones, smart devices, stick computers, secure tokens or etc. It could be used to record, sense, store data of user's context or environment surrounding him. The most common contextual information is user's location. Personal data generated and stored on these devices is valuable for many applications or services to user, but it is sensitive and needs to be protected in order to ensure the individual privacy. In particular, most mobile applications have access to accurate and real-time location information, raising serious privacy concerns for their users.In this dissertation, we dedicate the two parts to manage the location traces, i.e. the spatio-temporal data on mobile devices. In particular, we offer an extension of spatio-temporal data types and operators for embedded environments. These data types reconcile the features of spatio-temporal data with the embedded requirements by offering an optimal data presentation called Spatio-temporal object (STOB) dedicated for embedded devices. More importantly, in order to optimize the query processing, we also propose an efficient indexing technique for spatio-temporal data called TRIFL designed for flash storage. TRIFL stands for TRajectory Index for Flash memory. It exploits unique properties of trajectory insertion, and optimizes the data structure for the behavior of flash and the buffer cache. These ideas allow TRIFL to archive much better performance in both Flash and magnetic storage compared to its competitors.Additionally, we also investigate the protect user's sensitive information in the remaining part of this thesis by offering a privacy-aware protocol for participatory sensing applications called PAMPAS. PAMPAS relies on secure hardware solutions and proposes a user-centric privacy-aware protocol that fully protects personal data while taking advantage of distributed computing. For this to be done, we also propose a partitioning algorithm an aggregate algorithm in PAMPAS. This combination drastically reduces the overall costs making it possible to run the protocol in near real-time at a large scale of participants, without any personal information leakage.Aujourd'hui, les progrès dans le développement d'appareils mobiles et des capteurs embarqués ont permis un essor sans précédent de services à l'utilisateur. Dans le même temps, la plupart des appareils mobiles génèrent, enregistrent et de communiquent une grande quantité de données personnelles de manière continue. La gestion sécurisée des données personnelles dans les appareils mobiles reste un défi aujourd’hui, que ce soit vis-à-vis des contraintes inhérentes à ces appareils, ou par rapport à l’accès et au partage sûrs et sécurisés de ces informations. Cette thèse adresse ces défis et se focalise sur les traces de localisation. En particulier, s’appuyant sur un serveur de données relationnel embarqué dans des appareils mobiles sécurisés, cette thèse offre une extension de ce serveur à la gestion des données spatio-temporelles (types et operateurs). Et surtout, elle propose une méthode d'indexation spatio-temporelle (TRIFL) efficace et adaptée au modèle de stockage en mémoire flash. Par ailleurs, afin de protéger les traces de localisation personnelles de l'utilisateur, une architecture distribuée et un protocole de collecte participative préservant les données de localisation ont été proposés dans PAMPAS. Cette architecture se base sur des dispositifs hautement sécurisés pour le calcul distribué des agrégats spatio-temporels sur les données privées collectées

Sciunits: Reusable Research Objects

Science is conducted collaboratively, often requiring knowledge sharing about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. In this paper, we present the sciunit, a reusable research object in which aggregated content is recomputable. We describe a Git-like client that efficiently creates, stores, and repeats sciunits. We show through analysis that sciunits repeat computational experiments with minimal storage and processing overhead. Finally, we provide an overview of sharing and reproducible cyberinfrastructure based on sciunits gaining adoption in the domain of geosciences

B-urns

The fringe of a B-tree with parameter $m$ is considered as a particular P\'olya urn with $m$ colors. More precisely, the asymptotic behaviour of this fringe, when the number of stored keys tends to infinity, is studied through the composition vector of the fringe nodes. We establish its typical behaviour together with the fluctuations around it. The well known phase transition in P\'olya urns has the following effect on B-trees: for $m\leq 59$, the fluctuations are asymptotically Gaussian, though for $m\geq 60$, the composition vector is oscillating; after scaling, the fluctuations of such an urn strongly converge to a random variable $W$. This limit is $\mathbb C$-valued and it does not seem to follow any classical law. Several properties of $W$ are shown: existence of exponential moments, characterization of its distribution as the solution of a smoothing equation, existence of a density relatively to the Lebesgue measure on $\mathbb C$, support of $W$. Moreover, a few representations of the composition vector for various values of $m$ illustrate the different kinds of convergence

Utilizing Provenance in Reusable Research Objects

Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. Computational provenance is often the key to enable such reuse. In this paper, we show how reusable research objects can utilize provenance to correctly repeat a previous reference execution, to construct a subset of a research object for partial reuse, and to reuse existing contents of a research object for modified reuse. We describe two methods to summarize provenance that aid in understanding the contents and past executions of a research object. The first method obtains a process-view by collapsing low-level system information, and the second method obtains a summary graph by grouping related nodes and edges with the goal to obtain a graph view similar to application workflow. Through detailed experiments, we show the efficacy and efficiency of our algorithms.Comment: 25 page

Efficient management and secure sharing of mobility traces

Aujourd'hui, les progrès dans le développement d'appareils mobiles et des capteurs embarqués ont permis un essor sans précédent de services à l'utilisateur. Dans le même temps, la plupart des appareils mobiles génèrent, enregistrent et de communiquent une grande quantité de données personnelles de manière continue. La gestion sécurisée des données personnelles dans les appareils mobiles reste un défi aujourd’hui, que ce soit vis-à-vis des contraintes inhérentes à ces appareils, ou par rapport à l’accès et au partage sûrs et sécurisés de ces informations. Cette thèse adresse ces défis et se focalise sur les traces de localisation. En particulier, s’appuyant sur un serveur de données relationnel embarqué dans des appareils mobiles sécurisés, cette thèse offre une extension de ce serveur à la gestion des données spatio-temporelles (types et operateurs). Et surtout, elle propose une méthode d'indexation spatio-temporelle (TRIFL) efficace et adaptée au modèle de stockage en mémoire flash. Par ailleurs, afin de protéger les traces de localisation personnelles de l'utilisateur, une architecture distribuée et un protocole de collecte participative préservant les données de localisation ont été proposés dans PAMPAS. Cette architecture se base sur des dispositifs hautement sécurisés pour le calcul distribué des agrégats spatio-temporels sur les données privées collectées.Nowadays, the advances in the development of mobile devices, as well as embedded sensors have permitted an unprecedented number of services to the user. At the same time, most mobile devices generate, store and communicate a large amount of personal information continuously. While managing personal information on the mobile devices is still a big challenge, sharing and accessing these information in a safe and secure way is always an open and hot topic. Personal mobile devices may have various form factors such as mobile phones, smart devices, stick computers, secure tokens or etc. It could be used to record, sense, store data of user's context or environment surrounding him. The most common contextual information is user's location. Personal data generated and stored on these devices is valuable for many applications or services to user, but it is sensitive and needs to be protected in order to ensure the individual privacy. In particular, most mobile applications have access to accurate and real-time location information, raising serious privacy concerns for their users.In this dissertation, we dedicate the two parts to manage the location traces, i.e. the spatio-temporal data on mobile devices. In particular, we offer an extension of spatio-temporal data types and operators for embedded environments. These data types reconcile the features of spatio-temporal data with the embedded requirements by offering an optimal data presentation called Spatio-temporal object (STOB) dedicated for embedded devices. More importantly, in order to optimize the query processing, we also propose an efficient indexing technique for spatio-temporal data called TRIFL designed for flash storage. TRIFL stands for TRajectory Index for Flash memory. It exploits unique properties of trajectory insertion, and optimizes the data structure for the behavior of flash and the buffer cache. These ideas allow TRIFL to archive much better performance in both Flash and magnetic storage compared to its competitors.Additionally, we also investigate the protect user's sensitive information in the remaining part of this thesis by offering a privacy-aware protocol for participatory sensing applications called PAMPAS. PAMPAS relies on secure hardware solutions and proposes a user-centric privacy-aware protocol that fully protects personal data while taking advantage of distributed computing. For this to be done, we also propose a partitioning algorithm an aggregate algorithm in PAMPAS. This combination drastically reduces the overall costs making it possible to run the protocol in near real-time at a large scale of participants, without any personal information leakage

Solving the user-role reachability problem in ARBAC with role hierarchy

International audienceAccess Control is becoming increasingly important for today's ubiquitous systems since it provides mechanism to prevent sensitive resources in the systems against unauthorized users. In access control models, the administration of access control policies is an important task that raises a crucial analysis problem: if a set of administrators can give a user an unauthorized access permission. We consider the analysis problem in the context of the Administrative Role-Based Access Control (ARBAC), the most widespread administrative model. One of the main assumptions of current analysis techniques is that the role hierarchy is constant and thus can be abstracted away that results in the bad scalability of analysis techniques. In this paper, we introduce three reductions to enable an available analysis technique, namely ASASPXL, to handle the user-role reachability problem with the presence of role hierarchy. An extensive experimentation reports the superiority of our reductions in comparison with the approach used in the literature

TRIFL: A Generic Trajectory Index for Flash Storage

International audienceDue to several important features, such as high performance, low power consumption, and shock resistance, NAND flash has become a very popular stable storage medium for embedded mobile devices, personal computers, and even enterprise servers. However, the peculiar characteristics of flash memory require redesigning the existing data storage and indexing techniques that were devised for magnetic hard disks. In this article, we propose TRIFL, an efficient and generic TRajectory Index for FLash. TRIFL is designed around the key requirements of trajectory indexing and flash storage. TRIFL is generic in the sense that it is efficient for both simple flash storage devices such as SD cards and more powerful devices such as solid state drives. In addition, TRIFL is supplied with an online self-tuning algorithm that allows adapting the index structure to the workload and the technical specifications of the flash storage device to maximize the index performance. Moreover, TRIFL achieves good performance with relatively low memory requirements, which makes the index appropriate for many application scenarios. The experimental evaluation shows that TRIFL outperforms the representative indexing methods on magnetic disks and flash disks

PPTM: Privacy-aware Participatory Traffic Monitoring Using Mobile Secure Probes

National audiencePrivacy became one of the main concerns in location-based services in general and in community-based traffic monitoring in particular. This demonstration presents a new approach for privacy preserving online traffic monitoring using mobile probes. It combines hardware and software solutions, and a secure protocol to collect, aggregate and share the traffic information

Mobile participatory sensing with strong privacy guarantees using secure probes

International audienceMobile participatory sensing (MPS) could benefit many application domains. A major domain is smart transportation, with applications such as vehicular traffic monitoring, vehicle routing, or driving behavior analysis. However, MPS’s success depends on finding a solution for querying large numbers of smart phones or vehicular systems, which protects user location privacy and works in real-time. This paper presents PAMPAS, a privacy-aware mobile distributed system for efficient data aggregation in MPS. In PAMPAS, mobile devices enhanced with secure hardware, called secure probes (SPs), perform distributed query processing, while preventing users from accessing other users’ data. A supporting server infrastructure (SSI) coordinates the inter-SP communication and the computation tasks executed on SPs. PAMPAS ensures that SSI cannot link the location reported by SPs to the user identities even if SSI has additional background information. Moreover, an enhanced version of the protocol, named PAMPAS+, makes the system robust even against advanced hardware attacks on the SPs. Hence, the risk of user location privacy leakage remains very low even for an attacker controlling the SSI and a few corrupted SPs. Our experimental results demonstrate that these protocols work efficiently on resource constrained SPs being able to collect the data, aggregate them, and share statistics or derive models in real-time

Un protocole basé sur des mobiles sécurisés pour une collecte participative de données spatiales en mobilité réellement anonyme

International audienceLa collecte mobile d’information géographique volontaire (communément appelée VGI) se développe rapidement, transformant les citoyens en capteurs. Elle s’applique à l’observation de phénomènes spatiaux, comme le trafic routier, la pollution atmosphérique ou le bruit. Cependant, la géolocalisation des participants soulève des craintes justifiées de violation de vie privée. Le succès du déploiement d’une telle collecte à large échelle dépend donc de la capacité à protéger les données personnelles des participants tout en permettant de traiter en temps réel de flux continus de données géolocalisées. Cet article présente PAMPAS, un système distribué de VGI respectueux de la vie privée et offrant l’agrégation efficace des données. PAMPAS se base sur des mobiles équipés d’une sécurité matérielle, appelés ici sondes sécurisées, qui calculent des requêtes distribuées tout en empêchant les utilisateurs d’accéder aux données des autres utilisateurs. Les échanges de données entre sondes sécurisées sont chiffrés et passent par un serveur dit de support. Nous proposons deux protocoles distribués: l’un pour le calcul d’agrégats spatiaux et l’autre pour le partitionnement spatial adaptatif utilisé notamment pour paralléliser le calcul d’agrégats. Les résultats des expérimentations et l’analyse de sécurité montrent que ces protocoles permettent de collecter, agréger et partager des statistiques ou des données dérivées en temps réel sans divulguer aucune donnée personnelle