Blockchain-based reporting protocols as a collective monitoring mechanism in DAOs

As a recent decentralized business model,Distributed Autonomous Organization(DAO)challenges the management paradigms of conventional organizations and represents the future of democracy-based systems.However,DAOs are facing challenges that can be divided into two categories,including policy-based and technical challenges.Detecting misbehaving nodes is a crucial challenge that is considered in both categories.Designing mechanisms to report malicious behaviors while maintaining anonymity is essential for the survival and efficiency of DAOs.In this paper,we clarify the problem,and introduce Blockchain-Based Anonymous Reporting(BBAR)as a collective monitoring mechanism in DAOs with its requirements and implementation methods.

Blockchain technology forecasting by patent analytics and text mining

Information technologies(ITs)have been playing an important role in improving our society,and the fast evolution of ITs creates a competitive environment not only for companies but also for regions.Hence,recognizing the future trend of technologies can be effective in decision-making with regard to technology selection and investment.Blockchain technology with its vast and impressive applications has received considerable attention from researchers,investors,and public agencies.The purpose of this research is to investigate blockchain technology to explore its trends according to their classification by use of the World Intellectual Property Organization(WIPO)database.Furthermore,we particularly evaluate the registered patents in the world's most well-known patent databases such as the USA patent database.We drew the current technology trends in blockchain patents by applying the text mining and clustering approach.The results represent that the registered patents in the USA patent database have been achieved in the growth phase.That means,attention to the blockchain is rising nowadays and most patents focused the cryptocurrencies and their applications in finance.However,blockchain technology is in the emergence phase and is evolving by researchers and inventors.

Matching user identities across social networks with limited profile data

Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair.