Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

An OAuth2-based protocol with strong user privacy preservation for smart city mobile e-Health apps

In the context of the Smart City concept, mobile e-Health applications can play a pivotal role towards the improvement of citizens’ quality of life, since they can enable citizens to access personalized e-Health services, without limitations on time and location. However, accessing personalized e-Health services through citizens’ mobile e-Health applications, running on their mobile devices, raises many privacy issues in terms of citizens’ identity and location. These privacy issues should be addressed so that citizens, concerned about privacy leakage, will embrace Smart City mobile e-Health applications and reap their benefits. Hence, in this paper we propose an OAuth2-based protocol with strong user privacy preservation that addresses these privacy issues. Our proposed protocol follows the OAuth2 protocol flow and integrates a pseudonym-based signature scheme and a delegation signature scheme into the user authentication phase of the OAuth2 protocol. The proposed protocol enables citizens authentication towards the servers providing personalized e-Health services, while preserving their privacy from malicious mobile applications and/or eavesdroppers. Moreover, the proposed protocol does not require to store sensitive information in the citizens’ mobile devices

Marketing to Children Through Online Targeted Advertising: Targeting Mechanisms and Legal Aspects

Many researchers and organizations, such as WHO and UNICEF, have raised awareness of the dangers of advertisements targeted at children. While most existing laws only regulate ads on television that may reach children, lawmakers have been working on extending regulations to online advertising and, for example, forbid (e.g., the DSA) or restrict (e.g., the COPPA) advertising based on profiling to children. At first sight, ad platforms such as Google seem to protect children by not allowing advertisers to target their ads to users who are less than 18 years old. However, this paper shows that other targeting features can be exploited to reach children. For example, on YouTube, advertisers can target their ads to users watching a particular video through placement-based targeting, a form of contextual targeting. Hence, advertisers can target children by placing their ads in children-focused videos. Through a series of ad experiments, we show that placement-based targeting is possible on children-focused videos and enables marketing to children. In addition, our ad experiments show that advertisers can use targeting based on profiling (e.g., interest, location, behavior) in combination with placement-based advertising on children-focused videos. We discuss the lawfulness of these two practices concerning DSA and COPPA. Finally, we investigate to which extent real-world advertisers are employing placement-based targeting to reach children with ads on YouTube. We propose a measurement methodology consisting of building a Chrome extension to capture ads and instrument six browser profiles to watch children-focused videos. Our results show that 7% of ads that appear in the children-focused videos we test use placement-based targeting. Hence, targeting children with ads on YouTube is not only hypothetically possible but also occurs in practice..

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

Coming in Warm: Qualitative Study and Concept Map to Cultivate Patient‐Centered Empathy in Emergency Care

Background Increased empathy may improve patient perceptions and outcomes. No training tool has been derived to teach empathy to emergency care providers. Accordingly, we engaged patients to assist in creating a concept map to teach empathy to emergency care providers. Methods We recruited patients, patient caretakers and patient advocates with emergency department experience to participate in three separate focus groups (n = 18 participants). Facilitators guided discussion about behaviors that physicians should demonstrate in order to rapidly create trust, enhance patient perception that the physician understood the patient's point of view, needs, concerns, fears, and optimize patient/caregiver understanding of their experience. Verbatim transcripts from the three focus groups were read by the authors and by consensus, 5 major themes with 10 minor themes were identified. After creating a codebook with thematic definitions, one author reviewed all transcripts to a library of verbatim excerpts coded by theme. To test for inter‐rater reliability, two other authors similarly coded a random sample of 40% of the transcripts. Authors independently chose excerpts that represented consensus and strong emotional responses from participants. Results Approximately 90% of opinions and preferences fell within 15 themes, with five central themes: Provider transparency, Acknowledgement of patient's emotions, Provider disposition, Trust in physician, and Listening. Participants also highlighted the need for authenticity, context and individuality to enhance empathic communication. For empathy map content, patients offered example behaviors that promote perceptions of physician warmth, respect, physical touch, knowledge of medical history, explanation of tests, transparency, and treating patients as partners. The resulting concept map was named the “Empathy Circle”. Conclusions Focus group participants emphasized themes and tangible behaviors to improve empathy in emergency care. These were incorporated into the “Empathy Circle”, a novel concept map that can serve as the framework to teach empathy to emergency care providers

I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website