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Blockchain, the underlying technology of Bitcoin, refers to the public ledger used in a distributed network. Because blockchain does not rely on a central authority, peers have to agree on the state of the ledger among themselves, i.e., they have to reach a consensus on the state of the transactions. The way nodes reach that consensus has gained incredible attention in the literature. Bitcoin uses the Proof-of-Work (PoW) mechanism, as did Ethereum at first. The latter decided to move from PoW to Proof-of-Stake (PoS) because of the high energy consumption required by PoW. To date, many other consensus protocols have been proposed to address the limitations of the seminal ones. In this paper, we inform researchers and practitioners about the current state of consensus protocols research. The aim is to provide an analysis of the research introducing new consensus protocols in order to enable a more unified treatment. To that end, we review 28 new consensus protocols and we propose a four-category classification framework: Origin, Design, Performance and Security. We demonstrate the applicability of the framework by classifying the 28 protocols. Many surveys have already been proposed in the literature and some of them will be discussed later in the paper. Yet, we believe that this work is relevant and important for two reasons. Firstly, blockchain being a fast evolving topic, new consensus protocols emerge regularly and improvements are also put forward on a regular basis. Hence, this work aims at reflecting the latest state-of-the-art in terms of consensus protocols. Secondly, we aim to propose a comprehensive classification framework, integrating knowledge from multiple works in the literature, as well as introducing classification dimensions that have not been proposed before. This work demonstrates that multiple consensus have been proposed in a short period of time, and highlights the differences between these protocols. Furthermore, it is suggested that researchers and practitioners who aim to propose consensus protocols in the future should pay attention to all the dimensions presented in the classification framework.

In the last years social networks have emerged as a critical mean for information spreading bringing along several advantages. At the same time, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. For this reason, although the identification in social microblogs of which topic is a rumour has been studied in several works, there is the need to detect if a post is either a rumor or not. In this paper we cope with this last challenge presenting a novel rumour detection system that leverages on newly designed features, including influence potential and network characteristics measures. We tested our approach on a real dataset composed of health-related posts collected from Twitter microblog. We observe promising results, as the system is able to correctly detect about 90% of rumours, with acceptable levels of precision.

This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distributed data. Analyzing distributed data is essential in many applications such as medical, financial, and manufacturing data analyses due to privacy, and confidentiality concerns. In addition, interpretability of the obtained model has an important role for practical applications of the federated learning systems. By centralizing intermediate representations, which are individually constructed in each party, the proposed method obtains an interpretable model, achieving a collaborative analysis without revealing the individual data and learning model distributed over local parties. Numerical experiments indicate that the proposed method achieves better recognition performance for artificial and real-world problems than individual analysis., 16 pages, 3 figures, 3 tables