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Sensibilisation à la protection de la vie privée dans le cadre de l'utilisation de dispositifs portables intelligents (smart wearables)
Abstract
Les dispositifs portables intelligents (smart wearables) sont de plus en plus populaires dans les activités de sport, de loisirs, de gestion du temps et du calendrier. Leur potentiel est reconnu dans plusieurs scénarios. Plus précisément, leur capacité à colliger une foule d’informations au sujet de leur propriétaire et de son entourage par le biais des divers senseurs, peut éventuellement porter atteinte à sa vie privée.
Avec l’arrivée des « smart watches » sur le marché, nous nous retrouvons face à de véritables petits ordinateurs. Lorsque combinés à l’utilisation d’une application propriétaire résidant sur un smartphone/ordinateur et à l’utilisation d’une connexion directe (ou indirecte) à l’Internet, celles-ci permettent une agrégation importante de données.
Le but de ma recherche est donc dans un premier temps d’explorer les risques de sécurité relatifs aux dispositifs portables, dans le but d’en inférer les impacts sur la vie privée.
Dans une seconde partie, je propose de sonder un échantillon d’individus utilisant des smart wearables (pour le sport, loisirs ou la gestion de leur temps) et d’enquêter sur leur connaissance des différents risques d’atteinte à leur vie privée. A la fin de cette partie un pré-test sera construit.
Dans la troisième partie je mettrai en œuvre une session d’apprentissage/sensibilisation basée sur une application ludique qui sera suivie par un post-test. Cette dernière a pour but d’évaluer la capacité d’apprentissage et de rétention de mon public-cible.
... In fact, privacy competence is officially recognized in many countries, including Quebec, as a part of numerical competency. 2 Many classroom materials and curricula on online privacy have been developed recently (e. g. Common Sense Media, NetSafe and (Egelman et al. 2016;Fortin 2019). All of them are of high quality and considered as massive projects. ...
Nowadays, privacy education plays an important role in teenagers' lives. Since this domain is strongly linked to their social life, it is preferable to provide a collaborative learning environment that teaches privacy, and at the same time, allows students to share knowledge, to interact with each other, to solve quizzes collaboratively and to discuss privacy issues and situations. To this end, we propose "Teens-online", a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the International Competency Framework on Privacy Education. Moreover, the proposed platform is equipped with a partner-matching mechanism based on matching game theory. This mechanism guarantees a stable student-student matching according to the student's need (behavior and/or knowledge). Thus, mutual benefits will be attained by largely minimizing the chances of cooperating with incompatible students. Experimental results show that the average utility obtained by applying the proposed algorithm is much higher than the average utility obtained using other matching mechanisms. The results suggest that by adopting the proposed approach , each student can be paired with their optimal partners, which in turn can help them to engage more in learning activities.
Nowadays, with the increasing use of digital technologies, especially for teenagers, privacy
education plays an important role in their lives. While several e-learning platforms for privacy
awareness training have been implemented, they are typically based on traditional learning
techniques. In particular, these platforms do not allow students to cooperate and share
knowledge with each other in order to achieve mutual benefits and improve learning outcomes.
In other words, they lack student-student interaction. Recent research on learning methods
shows that the collaboration among students can result in better learning outcomes compared
to other learning approaches.
Motivated by the above-mentioned facts, and since privacy domain is strongly linked to the social
lives of teens, there is a pressing need for providing a collaborative learning platform for teaching
privacy, and at the same time, allows students to share knowledge, interact with each other, solve
quizzes collaboratively, and discuss privacy issues and situations.
For this purpose, this work proposes “Teens-online”, a collaborative e-learning platform for
privacy awareness. The curriculum provided in this platform is based on the Personal Data
Protection Competency Framework for School Students.
Moreover, the proposed platform is equipped with a partner-matching mechanism based on
matching game theory. This mechanism guarantees a stable student-student matching according
to a student's need (behavior and/or knowledge). Thus, mutual benefits will be attained by
minimizing the chances of cooperating with incompatible students.
Experimental results show that the average learning-related utility obtained by applying the
proposed partner-matching algorithm is much higher than the average utility obtained using
other matching mechanisms. The results also suggest that by adopting the proposed approach,
each student can be paired with their optimal partners, which in turn helps them reach their
highest learning outcomes.
The focus of this article is a threefold discussion on microlearning 1) how microlearning best practices facilitate knowledge acquisition in the workplace by engaging and motivating employees through short, personalized, just-in-time learning, 2) ways microlearning integrates with knowledge management applications through situational mentoring, and 3) how competency-based microlearning, via subscription learning, is both an innovative approach to e-learning and an asset to learning organizations focused on improving the performance of their employees. © 2018 Hong Kong Bao Long Accounting And Secretarial Limited. All rights reserved.
Wearable computing is rapidly getting deployed in many—commercial, medical, and personal—domains of day-to-day life. Wearable devices appear in various forms, shapes, and sizes and facilitate a wide variety of applications in many domains of life. However, wearables raise unique security and privacy concerns. Wearables also hold the promise to help enhance the existing security, privacy, and safety paradigms in unique ways while preserving the system’s usability.
The contribution of this research literature survey is threefold. First, as a background, we identify a wide range of existing as well as upcoming wearable devices and investigate their broad applications. Second, we provide an exposition of the security and privacy of wearable computing, studying dual aspects, that is, both attacks and defenses. Third, we provide a comprehensive study of the potential security, privacy, and safety enhancements to existing systems based on the emergence of wearable technology. Although several research works have emerged exploring different offensive and defensive uses of wearables, there is a lack of a broad and precise literature review systematizing all those security and privacy aspects and the underlying threat models. This research survey also analyzes current and emerging research trends and provides directions for future research.
In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned.
The pervasiveness of mobile devices such as smart phones, apps, remote monitoring devices, and wearable sensors is enabling growth of Patient Generated Health Data (PGHD) through which people are capturing their vital signs outside the clinical settings. Tracking fitness, helping with personal health issues, tracking diet and nutrition, tracking sleeping conditions, along with managing stress and mental health are touted as potential benefits of using wearable device services. However, following the trend of growth in electronic data breaches over the last few years, information privacy intrusion has become a major potential threat associated with collecting, tracking, storing, and sharing personal information. Drawing upon literature concerning privacy conceptualization, operationalization, and perception, we aim to explain the antecedents and outcomes of privacy concerns in the context of wearables to gain more insight about users' decisions on disclosing their personal health information. We may be on the crux of a golden age for personalized collaborative care through PGHD, yet we need to consider if we are doing so by trading-off privacy.
We propose a segment-based weighting technique for genre classification of web pages. This technique exploits character n-grams extracted from the URL of the web page rather than its textual content. The main idea of our technique is to segment the URL and assigns a weight for each segment. Experiments conducted on three known genre datasets show that our method achieves encouraging results.
We coin a new term called \textit{data transfusion} as a phenomenon that a user experiences when pairing a wearable device with the host device. A large amount of data stored in the host device (e.g., a smartphone) is forcibly copied to the wearable device (e.g., a smart watch) due to pairing while the wearable device is usually less attended. To the best of knowledge, there is no previous work that manipulates how sensitive data is transfused even without user's consent and how users perceive and behave regarding such a phenomenon for smart watches. We tackle this problem by conducting an experimental study of data extraction from commodity devices, such as in Android Wear, watchOS, and Tizen platforms, and a following survey study with 205 smart watch users, in two folds. The experimental studies have shown that a large amount of sensitive data was transfused, but there was not enough user notification. The survey results have shown that users have lower perception on smart watches for security and privacy than smartphones, but they tend to set the same passcode on both devices when needed. Based on the results, we perform risk assessment and discuss possible mitigation that involves volatile transfusion.
Bluetooth allows connecting mobile devices in short range in order to transfer files / videos. It is now a popular means of short range wireless communication. Although Bluetooth is acceptably reliable, there are still some weaknesses in this protocol. Bluetooth is vulnerable to several threats. Since these threats are seldom reported, people are not much aware of them. Existing surveys on Bluetooth security outlines only a few threats without much illustration and categorization. In this paper, we have performed a comprehensive survey to identify major security threats in Bluetooth communication and presented them with illustrations. Although Bluetooth device manufacturers are doing their part to keep the technology secure, the users should also be aware of these security threats and take a minimum level of precaution. The objective of this paper is to provide a comprehensive survey of existing threats in Bluetooth technology and suggest probable solutions.
The people-centric Internet of Things, as an essential part of the general IoT ecosystem, relies heavily on intelligent wearables to control and actuate the ambient/remote "things" according to the sensed information or collected instruction from the specific person. Intelligent wearables, although becoming increasingly prevalent and pervasive, are vulnerable to various cyber-attacks due to their intrinsic characteristics, which consequently brings up unprecedented security challenges in terms of privacy leakage, financial loss, and even malicious invasion of other connected IoT components and applications. Therefore, it is of great necessity to keep intelligent wearables reliable and trustworthy before being able to secure the core parts of IoT. Toward this end, we provide in this article a survey of available attack methodologies on intelligent wearables as well as the corresponding countermeasures, from the perspectives of data integrity, authenticity, and privacy.