385 research outputs found
Anonymity in Bitcoin? – The Users’ Perspective
This article analyzes how users perceive the degree of anonymity provided by the Bitcoin network, to what extent they are concerned about anonymity when using Bitcoin, whether they are knowledgeable of and concerned about specific de-anonymization attacks, and if they are aware of and adopt privacy-preserving countermeasures. A user survey with 125 active Bitcoin users reveals that 70% associate a medium or high level of anonymity with the Bitcoin network and rate their concerns as either low or medium. But almost every 5th user has already considered abandoning Bitcoin because of being concerned about anonymity. Though one third are aware of the risk of de-anonymizing the Blockchain but are not concerned, another almost 50% indeed feel concerned. Our findings have implications for users and developers, suggesting that actions should be undertaken to increase privacy awareness and the level of anonymity provided by the Blockchain and the Bitcoin network
Anonymization Technologies of Cryptocurrency Transactions as Money Laundering Instrument
This article is devoted to the exploration of services of anonymizing transactions, based on the Mixer, CoinJoin and CoinSuffle technologies, as well as to the description of the core principles of operation of these technologies and technical details. It analyzes the advantages and disadvantages of different realizations of this service. It formulates the problem of cryptocurrency laundering through anonymization services and offers solutions to this problem.Keywords: cryptocurrency, blockchain, bitcoin, mixing service, mining, money laundering
A Prevention and a Traction System for Ransomware Attacks
Over the past three years, especially following WannaCry malware, ransomware
has become one of the biggest concerns for private businesses, state, and local
government agencies. According to Homeland Security statistics, 1.5 million
ransomware attacks have occurred per year since 2016. Cybercriminals often use
creative methods to inject their malware into the target machines and use
sophisticated cryptographic techniques to hold hostage victims' files and
programs unless a certain amount of equivalent Bitcoin is paid. The return to
the cybercriminals is so high (estimated \$1 billion in 2019) without any cost
because of the advanced anonymity provided by cryptocurrencies, especially
Bitcoin \cite{Paquet-Clouston2019}. Given this context, this study first
discusses the current state of ransomware, detection, and prevention systems.
Second, we propose a global ransomware center to better manage our concerted
efforts against cybercriminals. The policy implications of the proposed study
are discussed in the conclusion section
Peer-to-Peer EnergyTrade: A Distributed Private Energy Trading Platform
Blockchain is increasingly being used as a distributed, anonymous, trustless
framework for energy trading in smart grids. However, most of the existing
solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy,
and traffic and processing overheads. In our previous work, we have proposed a
Secure Private Blockchain-based framework (SPB) for energy trading to address
the aforementioned challenges. In this paper, we present a proof-on-concept
implementation of SPB on the Ethereum private network to demonstrates SPB's
applicability for energy trading. We benchmark SPB's performance against the
relevant state-of-the-art. The implementation results demonstrate that SPB
incurs lower overheads and monetary cost for end users to trade energy compared
to existing solutions
BlockTag: Design and applications of a tagging system for blockchain analysis
Annotating blockchains with auxiliary data is useful for many applications.
For example, e-crime investigations of illegal Tor hidden services, such as
Silk Road, often involve linking Bitcoin addresses, from which money is sent or
received, to user accounts and related online activities. We present BlockTag,
an open-source tagging system for blockchains that facilitates such tasks. We
describe BlockTag's design and present three analyses that illustrate its
capabilities in the context of privacy research and law enforcement
Data mining for detecting Bitcoin Ponzi schemes
Soon after its introduction in 2009, Bitcoin has been adopted by
cyber-criminals, which rely on its pseudonymity to implement virtually
untraceable scams. One of the typical scams that operate on Bitcoin are the
so-called Ponzi schemes. These are fraudulent investments which repay users
with the funds invested by new users that join the scheme, and implode when it
is no longer possible to find new investments. Despite being illegal in many
countries, Ponzi schemes are now proliferating on Bitcoin, and they keep
alluring new victims, who are plundered of millions of dollars. We apply data
mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our
starting point is a dataset of features of real-world Ponzi schemes, that we
construct by analysing, on the Bitcoin blockchain, the transactions used to
perform the scams. We use this dataset to experiment with various machine
learning algorithms, and we assess their effectiveness through standard
validation protocols and performance metrics. The best of the classifiers we
have experimented can identify most of the Ponzi schemes in the dataset, with a
low number of false positives
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