Quality of Experience (QoE) has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modelling QoE for a variety of media services. The next logical step is to actively exploit that accumulated knowledge to improve and manage the quality of multimedia services, while at the same time ensuring efficient and cost-effective network operations. Moreover, with many different players involved in the end-to-end service delivery chain, identifying the root causes of QoE impairments and finding effective solutions for meeting the end users’ requirements and expectations in terms of service quality is a challenging and complex problem. In this article, we survey state-of-the-art findings and present emerging concepts and challenges related to managing QoE for networked multimedia services. Going beyond a number of previously published survey articles addressing the topic of QoE management, we address QoE management in the context of ongoing developments, such as the move to softwarized networks, the exploitation of big data analytics and machine learning, and the steady rise of new and immersive services (e.g., augmented and virtual reality). We address the implications of such paradigm shifts in terms of new approaches in QoE modeling and the need for novel QoE monitoring and management infrastructures.

Abstract. Disclosing personal information in online social network services is a double-edged sword. Information exposure is usually a plus, even a must, if people want to participate in social communities; however, leakage of personal information, especially one's identity, may invite malicious attacks from the real world and cyberspace, such as stalking, reputation slander, personalized spamming and phishing. Even if people do not reveal their personal information online, others may do so. In this paper, we consider the problem of involuntary information leakage in social network services and demonstrate its seriousness with a case study of Wretch, the biggest social network site in Taiwan. Wretch allows users to annotate their friends' profiles with a one-line description, from which a friend's private information, such as real name, age, and school attendance records, may be inferred without the information owner's knowledge. Our analysis results show that users' efforts to protect their privacy cannot prevent their personal information from being revealed online. In 592, 548 effective profiles that we collected, the first name of 72% of the accounts and the full name of 30% of the accounts could be easily inferred by using a number of heuristics. The age of 15% of the account holders and at least one school attended by 42% of the holders could also be inferred. We discuss several potential means of mitigating the identified involuntary information leakage problem.

Abstract. Many visual similarity-based phishing page detectors have been developed to detect phishing webpages, however, scammers now create polymorphic phishing pages to breach the defense of those detectors. We call this kind of countermeasure phishing page polymorphism. Polymorphic pages are visually similar to genuine pages they try to mimic, but they use different representation techniques. It increases the level of difficulty to detect phishing pages. In this paper, we propose an effective detection mechanism to detect polymorphic phishing pages. In contrast to existing approaches, we analyze the layout of webpages rather than the HTML codes, colors, or content. Specifically, we compute the similarity degree of a suspect page and an authentic page through image processing techniques. Then, the degrees of similarity are ranked by a classifier trained to detect phishing pages. To verify the efficacy of our phishing detection mechanism, we collected 6, 750 phishing pages and 312 mimicked targets for the performance evaluation. The results show that our method achieves an excellent detection rate of 99.6%.

Generic relationships between QoE and QoS have been intensively discussed in literature for single QoS parameters and often found to be logarithmic or exponential. While there are many experimental studies investigating statistically the influence of several parameters on QoE, the generic relationship between them, and how to best model it, have not been discussed so far. For communication networks, however, there is a major interest from different stakeholders to have multi-dimensional QoE models. The contribution of this paper is an analysis of the generic relationship between QoS and QoE for multiple QoS parameters and its implications. We address the question of whether multi-dimensional QoE models for several parameters are additive or multiplicative. In an analytic model and with examples involving HTTP non-adaptive video streaming, we show that a multiplicative model has different properties than the current additive QoE model proposed in ITU-T standards. We want to raise sensitivity in the community on multi-factor QoE models, their properties, and the need for multi-factor studies to confirm the appropriate models.

Abstract-In recent disasters, the web has served as a medium of communication among disaster response teams, survivors, local citizens, curious onlookers, and zealous people who are willing to assist victims affected by disasters. To encourage and speed up information dissemination, the availability and convenience of use are normally the top concerns in designing disaster response web services, where a design of free-formed inputs without access control is commonly adopted. However, such design may result in personal information disclosure and privacy leakage.In this paper, using a case study of a real-life disaster response service, the MKER (Morakot Event Reporting) forum, we show that the disclosure of personal information and the resulting privacy disclosure is indeed a serious problem that is currently happening. In our case, we have successfully mapped 1, 438 unique cell phone numbers and 1, 383 unique addresses to individuals using an automated method, not to mention the much greater invasion of privacy that could be effected by manual analysis of the messages posted on the forum. To resolve this issue, we propose several means to mitigate and prevent the mentioned privacy leakage on disaster response services from being happened.