Security and Privacy in Pervasive Computing State of the Art and Future Directions

Author(s):  
Dieter Hutter ◽  
Werner Stephan ◽  
Markus Ullmann
2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.


2016 ◽  
Vol 17 (13) ◽  
pp. 1455-1470 ◽  
Author(s):  
Tomas Majtan ◽  
Angel L. Pey ◽  
June Ereño-Orbea ◽  
Luis Alfonso Martínez-Cruz ◽  
Jan P. Kraus

2019 ◽  
Vol 14 ◽  
Author(s):  
Khaleel Ahmad ◽  
Afsar Kamal

Background: Privacy enhancing techniques are developed in order to provide strong protection to cyberspace. These techniques aim to allow users to keep their identities hidden during the communication when they are sending email, making payments online, browsing the Web or posting to newsgroups. MixNet is the most practical solution for concealing identities of message and sender’s identities. Objective: It provides sender and receiver anonymity as well as message security. The main goal of MixNet is to overcome vulnerability and enhance the performance of communication. It can efficiently handle the messages of various length and produce desirable results with privacy. The main objective of this paper is to acquire information and concepts regarding MixNet. We also provide guidelines for future research and references. Methods : The designing of MixNet depends on what cryptosystem method is used. Symmetric and Asymmetric both are used. Other methods could be also used such as PIR, CSP and FDR model, RPC, Token-based approach or others. Result: In this paper we provide an overview of MixNet approaches and presented a survey on MixNet based techniques and different models. We also constructed the comparison tables as per our requirements for better understanding. For this purpose, we found a total of 120 articles related to the MixNet published between 1990 and 2018 from the most relevant scientific resources. However, only 86 papers were analyzed due to comprehensiveness and relevancy to this article. Conclusion: Focused on the security and privacy of communication and how it can be achieved. This survey also reveals research progress and research gap on MixNet.


Author(s):  
Alvaro Gomez-Lopez ◽  
Satyannarayana Panchireddy ◽  
Bruno Grignard ◽  
Inigo Calvo ◽  
Christine Jerome ◽  
...  

2021 ◽  
Vol 54 (2) ◽  
pp. 1-42
Author(s):  
Abdullah Qasem ◽  
Paria Shirani ◽  
Mourad Debbabi ◽  
Lingyu Wang ◽  
Bernard Lebel ◽  
...  

In the era of the internet of things (IoT), software-enabled inter-connected devices are of paramount importance. The embedded systems are very frequently used in both security and privacy-sensitive applications. However, the underlying software (a.k.a. firmware) very often suffers from a wide range of security vulnerabilities, mainly due to their outdated systems or reusing existing vulnerable libraries; which is evident by the surprising rise in the number of attacks against embedded systems. Therefore, to protect those embedded systems, detecting the presence of vulnerabilities in the large pool of embedded devices and their firmware plays a vital role. To this end, there exist several approaches to identify and trigger potential vulnerabilities within deployed embedded systems firmware. In this survey, we provide a comprehensive review of the state-of-the-art proposals, which detect vulnerabilities in embedded systems and firmware images by employing various analysis techniques, including static analysis, dynamic analysis, symbolic execution, and hybrid approaches. Furthermore, we perform both quantitative and qualitative comparisons among the surveyed approaches. Moreover, we devise taxonomies based on the applications of those approaches, the features used in the literature, and the type of the analysis. Finally, we identify the unresolved challenges and discuss possible future directions in this field of research.


2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.


2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.


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