The emergence of Internet protocol suites and packet-switching technologies tend to considerations of security, privacy, scalability, and reliability in layered Internet service architectures. The existing service systems allow us to access big data, but few studies focus on the fundamental security and stability in these systems, especially when they involve large-scale networks with overloaded private information. In this research, we explored the blockchain-based mechanism that aims to improve the critical features of traditional Internet services, including autonomous and decentralized processing, smart contractual enforcement of goals, and traceable trustworthiness in tamper-proof transactions. Furthermore, we provide a comprehensive review to conceptualize the blockchain-based framework to develop decentralized protocols for the extensive number of Internet services. This comprehensive survey aims to address blockchain integration to secure Internet services and identify the critical requirements of developing a decentralized trustworthy Internet service. Additionally, we present a case study of block-chain based IoT for neuro-informatics to illustrate the potential applications of blockchain architectures. Finally, we summarize the trends and challenges of blockchain architectures that benefit a multitude of disciplines across all internet service fields of interest.

Users in online social networking sites unknowingly disclose their sensitive information that aggravate the social and financial risks. Hence, to prevent the information loss and privacy exposure, users need to find ways to quantify their privacy level based on their online social network data. Current studies that focus on measuring the privacy risk and disclosure consider only a single source of data, neglecting the fact that users, in general, can have multiple social network accounts disclosing different sensitive information. In this paper, we investigate an approach that can help social media users to measure their privacy disclosure score (PDS) based on the information shared across multiple social networking sites. In particular, we identify the main factors that have impact on users privacy, namely, sensitivity and visibility, to obtain the final disclosure score for each user. By applying the statistical and fuzzy systems, we can specify the potential information loss for a user by using obtained PDS. Our evaluation results with real social media data show that our method can provide a better estimation of privacy disclosure score for users having presence in multiple online social networks.

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

Brain activity analysis is an important research area in the field of human neuroscience. Moreover, a subcategory in this field is the classification of brain activity in terms of different brain disorders. Since the Electroencephalography (EEG) sign

Analysis of human brain activity is an important topic in human neuroscience. Human brain activity can be studied by analyzing the electroencephalography (EEG) signal. In this way, scientists have employed several techniques that investigate nonlinea