Global ETD Search

1	Privacy Notice and Choice in Practice Leon-Najera, Pedro Giovanni 01 December 2014 (has links) In the United States, notice and choice remain the most commonly used mechanisms to protect people’s privacy online. This approach relies on the assumption that users provided with notice will make informed choices that align with their privacy expectations. The goal of this research is to empirically inform industry and regulatory efforts that rely on notice and choice to protect people’s online privacy. To do so, we present a set of case studies covering different aspects of privacy notice and choice in four domains: online behavioral advertising (OBA), online social networks (OSN), financial privacy notices, and websites’ machine-readable privacy notices. We investigate users’ privacy preferences, information needs, and ability to exercise choices in the OBAdomain. Based on our results, we provide recommendations to improve the design of notice and choice methods currently in use in this domain. In the context of OSNs, we explore the effect of nudging notices designed to encourage more thoughtful disclosures among Facebook users and recommend changes to the Facebook user interface aimed to mitigate problematic disclosures. We demonstrate how standardized notices enable large-scale evaluations and comparisons of companies’ privacy practices and argue that standardized privacy notices have an enormous potential to improve transparency and benefit users, privacy-respectful companies, and oversight entities. We argue that, in today’s complex Internet ecosystem, an approach that relies on users to make privacy decisions should also empower them with user-friendly interfaces, relevant information, and the tools they need to make privacy decisions. Finally, we further argue that notice and choice are necessary, but not sufficient to protect online privacy, and that government regulation is necessary to establish necessary additional protections including access, redress, accountability, and enforcement. behavioral advertising human-computer interactions online tracking privacy choice privacy disclosure privacy notice
2	Browser Fingerprinting : Exploring Device Diversity to Augment Authentification and Build Client-Side Countermeasures / Empreinte digitale d'appareil : exploration de la diversité des terminaux modernes pour renforcer l'authentification en ligne et construire des contremesures côté client Laperdrix, Pierre 03 October 2017 (has links) L'arrivée de l'Internet a révolutionné notre société à l'aube du 21e siècle. Nos habitudes se sont métamorphosées pour prendre en compte cette nouvelle manière de communiquer el de partager avec le monde. Grâce aux technologies qui en constituent ses fondations, le web est une plateforme universelle Que vous utilisiez un PC de bureau sous Windows, un PC portable sous MacOS, un serveur sous Linux ou une tablette sous Android, chacun a les moyens de se connecter à ce réseau de réseaux pour partager avec le monde. La technique dite de Browser fingerprinting est née de celle diversité logicielle et matérielle qui compose nos appareils du quotidien. En exécutant un script dans le navigateur web d'un utilisateur, un serveur peut récupér une très grande quantité d'informations. Il a été démontré qu'il est possible d'identifier de façon unique un appareil en récoltant suffisamment d'informations. L'impact d'une telle approche sur la vie privée des internautes est alors conséquente, car le browser fingerprinting est totalement indépendant des systèmes de traçage connu comme les cookies. Dans celle thèse, nous apportons les contributions suivantes : une analyse de 118 934 empreintes, deux contre-mesures appelées Blink et FPRandom et un protocole d'authentification basé sur le canvas fingerprinting. Le browser fingerprinting est un domaine fascinant qui en est encore à ses balbutiements. Avec cette thèse, nous contribuons à l'écriture des premières pages de son histoire en fournissant une vue d'ensemble du domaine, de ses fondations jusqu'à l'impact des nouvelles technologies du web sur cette technique. Nous nous tournons aussi vers le futur via l'exploration d'une nouvelle facette du domaine afin d'améliorer la sécurité des comptes sur Internet. / Users are presented with an ever-increasing number of choices to connect to the Internet. From desktops, laptops, tablets and smartphones, anyone can find the perfect device that suits his or her needs while factoring mobility, size or processing power. Browser fingerprinting became a reality thanks to the software and hardware diversity that compose every single one of our modem devices. By collecting device-specific information with a simple script running in the browser, a server can fully or partially identify a device on the web and follow it wherever it goes. This technique presents strong privacy implications as it does not require the use of stateful identifiers like cookies that can be removed or managed by the user. In this thesis, we provide the following contributions: an analysis of 118,934 genuine fingerprints to understand the current state of browser fingerprinting, two countermeasures called Blink and FPRandom and a complete protocol based on canvas fingerprinting to augment authentication on the web. Browser fingerprinting is still in its early days. As the web is in constant evolution and as browser vendors keep pushing the limits of what we can do online, the contours of this technique are continually changing. With this dissertation, we shine a light into its inner-workings and its challenges along with a new perspective on how it can reinforce account security. Browser fingerprinting Diversité logicielle Authentification Traçage sur internet Browser fingerprinting Software diversity Authentication Online tracking 005.8
3	Illegal Cookie Banners and Developing a Compliant Solution Sandin, Arvid January 2024 (has links) Laws in the European Union can be difficult to interpret and the General Data Protection Regulation (GDPR) has majorly redefined consent in regard to online tracking. By specifying requirements for a cookie banner, the compliance of different websites can easier be investigated and a compliant cookie banner can be created. The result shows that virtually all websites fail to collect consent in accordance with the law. A created web component, simply called cookie-banner is suggested as a compliant solution. Cookies GDPR Cookie banner Online tracking Kakor Dataskyddsförordningen Computer Engineering Datorteknik
4	Exploiting scene context for on-line object tracking in unconstrained environments / Exploitation du contexte de scène pour le suivi d’objet en ligne dans des environnements non contraints Moujtahid, Salma 03 November 2016 (has links) Avec le besoin grandissant pour des modèles d’analyse automatiques de vidéos, le suivi visuel d’objets est devenu une tache primordiale dans le domaine de la vision par ordinateur. Un algorithme de suivi dans un environnement non contraint fait face à de nombreuses difficultés: changements potentiels de la forme de l’objet, du fond, de la luminosité, du mouvement de la camera, et autres. Dans cette configuration, les méthodes classiques de soustraction de fond ne sont pas adaptées, on a besoin de méthodes de détection d’objet plus discriminantes. De plus, la nature de l’objet est a priori inconnue dans les méthodes de tracking génériques. Ainsi, les modèles d’apparence d’objets appris off-ligne ne peuvent être utilisés. L’évolution récente d’algorithmes d’apprentissage robustes a permis le développement de nouvelles méthodes de tracking qui apprennent l’apparence de l’objet de manière en ligne et s’adaptent aux variables contraintes en temps réel. Dans cette thèse, nous démarrons par l’observation que différents algorithmes de suivi ont différentes forces et faiblesses selon l’environnement et le contexte. Afin de surmonter les variables contraintes, nous démontrons que combiner plusieurs modalités et algorithmes peut améliorer considérablement la performance du suivi global dans les environnements non contraints. Plus concrètement, nous introduisant dans un premier temps un nouveau framework de sélection de trackers utilisant un critère de cohérence spatio-temporel. Dans ce framework, plusieurs trackers indépendants sont combinés de manière parallèle, chacun d’entre eux utilisant des features bas niveau basée sur différents aspects visuels complémentaires tel que la couleur, la texture. En sélectionnant de manière récurrente le tracker le plus adaptée à chaque trame, le système global peut switcher rapidement entre les différents tracker selon les changements dans la vidéo. Dans la seconde contribution de la thèse, le contexte de scène est utilisé dans le mécanisme de sélection de tracker. Nous avons conçu des features visuelles, extrait de l’image afin de caractériser les différentes conditions et variations de scène. Un classifieur (réseau de neurones) est appris grâce à ces features de scène dans le but de prédire à chaque instant le tracker qui performera le mieux sous les conditions de scènes données. Ce framework a été étendu et amélioré d’avantage en changeant les trackers individuels et optimisant l’apprentissage. Finalement, nous avons commencé à explorer une perspective intéressante où, au lieu d’utiliser des features conçu manuellement, nous avons utilisé un réseau de neurones convolutif dans le but d’apprendre automatiquement à extraire ces features de scène directement à partir de l’image d’entrée et prédire le tracker le plus adapté. Les méthodes proposées ont été évaluées sur plusieurs benchmarks publiques, et ont démontré que l’utilisation du contexte de scène améliore la performance globale du suivi d’objet. / With the increasing need for automated video analysis, visual object tracking became an important task in computer vision. Object tracking is used in a wide range of applications such as surveillance, human-computer interaction, medical imaging or vehicle navigation. A tracking algorithm in unconstrained environments faces multiple challenges : potential changes in object shape and background, lighting, camera motion, and other adverse acquisition conditions. In this setting, classic methods of background subtraction are inadequate, and more discriminative methods of object detection are needed. Moreover, in generic tracking algorithms, the nature of the object is not known a priori. Thus, off-line learned appearance models for specific types of objects such as faces, or pedestrians can not be used. Further, the recent evolution of powerful machine learning techniques enabled the development of new tracking methods that learn the object appearance in an online manner and adapt to the varying constraints in real time, leading to very robust tracking algorithms that can operate in non-stationary environments to some extent. In this thesis, we start from the observation that different tracking algorithms have different strengths and weaknesses depending on the context. To overcome the varying challenges, we show that combining multiple modalities and tracking algorithms can considerably improve the overall tracking performance in unconstrained environments. More concretely, we first introduced a new tracker selection framework using a spatial and temporal coherence criterion. In this algorithm, multiple independent trackers are combined in a parallel manner, each of them using low-level features based on different complementary visual aspects like colour, texture and shape. By recurrently selecting the most suitable tracker, the overall system can switch rapidly between different tracking algorithms with specific appearance models depending on the changes in the video. In the second contribution, the scene context is introduced to the tracker selection. We designed effective visual features, extracted from the scene context to characterise the different image conditions and variations. At each point in time, a classifier is trained based on these features to predict the tracker that will perform best under the given scene conditions. We further improved this context-based framework and proposed an extended version, where the individual trackers are changed and the classifier training is optimised. Finally, we started exploring one interesting perspective that is the use of a Convolutional Neural Network to automatically learn to extract these scene features directly from the input image and predict the most suitable tracker. Informatique Vision par ordinateur Apprentissage automatique Traitement d'images Suivi d'objet Temps réel IT - Information Technology Computer vision Machine learning Online tracking Real time 006.370 72
5	Osobní údaje a problémy personalizace / Personal data and personalization issues Farafonov, Gerasim January 2012 (has links) Society is currently finding itself in the middle of an information revolution, and only now begins to realize this fact. The primary currency in the new economy is user data and the enormous value is evident only to companies doing business with them. This thesis is dedicated to the protection of personal data in the context of the currently developing trend of personalization. Author sets out tools that make online tracking possible, compares the legislative regulation of the Czech Republic, the Russian Federation and the United States of America, evaluating the differences, trends, strengths and weaknesses in relation to the practice of creating profiles and behavioural targeting advertising. Subsequently, the author lists the additional risks of this trend for the freedom and privacy, lists general recommendations as well as recommendations for the users themselves to maximize the protection of privacy and personal data.
6	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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