Paper Reading on Bitcoin Anonymity

An attacker wishing to de-anonymize its users will attempt to construct the one-to-many mapping between users and public-keys and associate
information external to the system with the users. Bitcoin frustrates this attack by storing the mapping of a user to his or her public-keys on that user’s node only and by allowing each user to generate as many public-keys as required.


However, this flexibility comes at a price: the entire history of Bitcoin transactions is publicly available. 
In this paper we investigated the structure of two networks derived from this dataset and their implications for user anonymity

Consequently, Bitcoin has the unintuitive property that while the ownership of money is implicitly anonymous, its flow is globally visible. In this paper we explore
this unique characteristic further, using heuristic clustering to group Bitcoin wallets based on evidence of shared authority, and then using re-identification attacks

We downloaded the full history of this scheme, and analyzed many statistical properties of its associated transaction
graph. In this paper we answer for the rst time a variety of interesting questions about the typical behavior of users

The Bitcoin system is the best known and most widely used alternative payment scheme, but so far it was very difficult to get accurate information about how
it is used in practice. In this paper we describe a large number of statistical properties of the Bitcoin transaction graph

We perform a comprehensive measurement analysis of Silk Road, an anonymous, international online marketplace that operates as a Tor hidden service and uses Bitcoin as its exchange currency

However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly
announced in the system.

In this paper, we investigate the privacy provisions in Bitcoin when it is used as a primary currency to support the daily transactions of individuals in a university
setting. More specifically...

Our findings show that the current measures adopted by Bitcoin are not enough to protect the privacy of users if Bitcoin were to be used as a digital currency in a university setting.

In the currently used Bitcoin clients, the full transaction history is available at each node of the network to prevent double spending without the need for a central authority, forming a valuable source for empirical research on network structure, network dynamics, and the implied anonymity challenges, as well as guidance on the future evolution of complex payment systems. We found dynamical effects of which some increase anonymity while others decrease it.

This chapter describes a graph-based method for analyzing the identity clustering and currency flow properties of Bitcoin transactions.


The graph-based method for analyzing the identity clustering and currency flow properties of Bitcoin transactions can expedite forensic investigations of criminal cases such as Mt. Gox that involve doublespending, theft, money laundering or fraudulent transactions.

However, the complete history of all transactions ever performed, called \blockchain",is public and replicated on each node. The data it contains is difficult to
analyze manually, but can yield a high number of relevant information.In this paper we present a modular framework, BitIodine, which parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classies such users and labels them, and nally visualizes complex information extracted from the Bitcoin network.

Zerocoin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment’s origin. Yet, it still
reveals payments’ destinations and amounts, and is limited in functionality.
In this paper, we construct a full-fledged ledger-based digital currency with strong privacy guarantees.

Although previous work has analyzed the degree of anonymity Bitcoin offers using clustering and flow analysis, 
none have demonstrated the ability to map Bitcoin addresses directly to IP data. We propose a novel approach to ...

We present the first empirical analysis of Bitcoin-based scams: operations established with fraudulent intent.

While Bitcoin provides some level of anonymity (or rather pseudonymity) by encouraging the users to have any number
of random-looking Bitcoin addresses, recent research shows that this level of anonymity is rather low.

While Bitcoin's presumed anonymity offers new avenues for commerce, several recent studies raise user-privacy concerns. 
We explore the level of anonymity in the Bitcoin system. Our approach is two-fold:...


In conclusion, we showed that by leveraging several sources of publicly available information via web-scraped forums and Bitcoin's transaction ledger, 
the bitcoin transaction network is shown to be not entirely anonymous.

We analyse the performance of several clustering algorithms in the digital peer-to-peer currency Bitcoin.
Our results demonstrate that even modern wallet software can not protect its users properly.


Our objective for this project is to assess the effect of blockchain analysis on theprivacy of Bitcoin participants.

In this paper, we explore the role of privacy-enhancing overlays in Bitcoin.

In this paper, we presented a denitional framework for the anonymity that virtual currencies such as Bitcoin provide. We then provided constructive solutions
for achieving this new notion of anonymity, and analyzed the extent to which it was already being achieved by existing Bitcoin overlays.

We perform a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim’s files until a ransom
is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014.

Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on
the micro-structure of transactions have proved very effective in practice.In this paper we describe the primary reasons behind this effectiveness

One of its most important properties is the high level of anonymity it provides for its users.
Monitoring the propagation of these messages and analyzing them carefully reveal hidden relations. 
In this paper,we develop a mathematical model using ...

While Bitcoin provides a signicant level of anonymity as Bitcoin addresses can be generated freely and without providing
any form of personal identication, the requirement to announce new transactions on the peer to peer network opens up the possibility of 
linking Bitcoin addresses to the IP addresses of clients.

It is important to know the in depth structure of blockchain by identifying common behaviors of the transactions and the effect of these behaviors on the nodes of the network. Dominant set approach can categorize the blockchain transactions into different clusters without mentioning number of clusters in advance.

This exponential growth in the market value of Bitcoin motivates adversaries to exploit weaknesses for profit, 
and researchers to discover new vulnerabilities in the system, propose countermeasures, and predict upcoming trends.
In this paper, we present a systematic survey that covers the security and privacy aspects of Bitcoin

However, blockchain faces its own problems and challenges. One key problem is to automatically cluster the behavior patterns of all the blockchain nodes
into categories. In this paper, we introduce the problem of behavior pattern clustering in blockchain networks and propose a novel algorithm termed BPC for this problem.


In this paper, we have introduced the problem of behavior pattern clustering in blockchain networks and have proposed a novel algorithm termed BPC to address this problem.

However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time.
We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike.

There is an ongoing debate on the question of users’ anonymity: while the Bitcoin protocol has been designed to ensure that the activity of individual users could not be tracked, some methods have been proposed to partially bypass this limitation. 
In this article, we show how the Bitcoin transaction network can be studied using complex networks analysis techniques, and in
particular how community detection can be efficiently used to re-identify multiple addresses belonging to a same user.

In this article, we have shown how community detection could be used to improve
re-identification of users in bitcoins.

Data analytics has recently enabled the uncovering of interesting properties of several complex networks. Among these, it is worth considering the bit-
coin blockchain, because of its peculiar characteristic of reflecting a niche, but also a real economy whose transactions are publicly available. In this paper we
present the analyses we have performed on the users graph inferred from the bitcoin blockchain

In Bitcoin user identification, an important challenge is to accurately link Bitcoin address to their owners.
In this paper, we propose a deep learning method to achieve address-user mapping.

After the test experiments of address verication, recognition and clustering on our test dataset, 
we conclude that the embedded address behaviors indeed conceal information of its owner.

In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization.

A number of studies have shown that, in this pseudonymous context,identities can be leaked based on transaction features or offnetwork
information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction.


We formulate the problem of analyzing Bitcoin Blockchain transaction graph anonymity properties as a classification problem over a set of categories of Bitcoin users.

A key challenge that blockchain faces is to precisely classify the blockchain peers into categories with respect to their behavior patterns, which will not only
enable deeper insights into the blockchain network but also facilitate more effective maintenance of the various peers (in private chains). In this paper, we introduce and formulate the problem of behavior pattern classification in blockchain networks and propose a novel deep-learning-based method

In this paper, we have introduced the problem of behavior pattern classification in blockchain networks and have proposed a novel deep learning based approach termed PeerClassifier to address this issue.

Bitcoin is gaining more and more popularity in criminal society. That is why Bitcoin is often used as money laundering tool or payment method for illegal products and services. In this paper we explore various methods for Bitcoin users deanonimization, which is an important task in anti-money laundering process and cybercrime investigation.


Bitcoin privacy is an emergent field of research, which is gaining more and more attention from researchers all over the world. Being a popular payment tool along
criminals, the society needs tools and suitable laws to fight with illegal usage of bitcoin.

Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities.

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.

However,
since all currency transfers are recorded on a public ledger, surveillance authorities can analyse them, trying to detect anomalous or suspect behaviours.

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we
introduce a complete probabilistic model of the Bitcoin Blockchain.

Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network.

The Bitcoin system is an anonymous, decentralized crypto-currency. There are some deanonymizating techniques to cluster Bitcoin addresses and to map them to users’ identifications in the two research directions of Analysis of Transaction Chain (ATC) and Analysis of Bitcoin Protocol and Network (ABPN).

Because of Bitcoin’s comparably high level of anonymity, it has been labelled as the go-to currency for illicit activity.
Our paper aims to better understand the different kinds of transactions in the Bitcoin ecosystem in order to better inform managerial and organizational aspects of regulation and compliance.

It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. 
This paper proposes an approach to mine the transaction networks of exchanges for answering this question.

Rooted from the nature of decentralization and anonymity of blockchain, the cryptocurrencies have, unfortunately, been leveraged for illicit activities by the criminals. The good news is that typical cryptocurrencies, such as Bitcoin, have to publicly publish their transactions, known as a graph, to retain their ultimate goal of trustless and decentralized transaction verification, which lends law enforcement a means to deanonymizing cryptocurrencies

The availability of the entire Bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analysing the transaction graph to obtain insight on users behaviour. 
This paper presents an analysis of the Bitcoin users graph, obtained by clustering the transaction graph, to highlight its connectivity structure and the economical meaning of the different obtained components

However,by analyzing the transaction pattern, tracking the fund flow, and associating addresses with real entities, it is possible to defeat this anonymity. One of the key challenges is scalability - it is difficult to produce useful results in a short time, while keeping up with terabytes of increasingly large blockchain data. We propose BITSCOPE, a de-anonymization system that uses a layered approach and exploits the domain-specific structures in Bitcoin transaction network.

Unfortunately, some criminal behaviors which took advantage of this platform were not identified. This has discouraged many governments to support
cryptocurrency. Thus, the capability to identify criminal addresses becomes an important issue in the cryptocurrency network. In this paper, we propose new features in addition to those commonly used in the literature to build a classification model for detecting abnormality of Bitcoin network addresses.

Yet, despite the fact that all financial transactions in Bitcoin are available in an openly accessible online ledger—the blockchain—not much is known about how different types of actors in the network (we call them entities) actually use Bitcoin. BitConduite offers an entity-centered view on transactions, making the data accessible to non-technical experts through a guided workflow for classification of entities according to several activity metrics.

However, the relation among bitcoin transactions can be used to analyze the bitcoin privacy information, which seriously jeopardizes the bitcoin anonymity. Herein, we describe the vulnerabilities associated with the anonymity mechanism of bitcoin, including the relation among bitcoin addresses and the relation among bitcoin users.

The anonymity and decentralization of Bitcoin make it widely accepted in illegal transactions, such as money laundering,drug and weapon trafficking, gambling, to name a few, which has already caused significant security risk all around the world. The obvious de-anonymity approach that matches transaction addresses and users is not possible in practice due to limited annotated data set. In this paper, we divide addresses into four types

However, many studies have identified several problems associated with the privacy and anonymity of Bitcoin. 
Consequently, a large number of attempts have been made to address these issues, yet it has been proven that many such solutions do not provide an acceptable level
of anonymity. This survey presents an account of the level of anonymity achieved through those and attempts to provide a comparative evaluation across different constructions.

Our findings showed that despite numerous attempts to achieve complete anonymity and privacy of cryptocurrencies, 
limitations exist in their practical implementations and hence they had been unable to realise true anonymity in practice.

However, it has been widely reported that it can be used for investment scams, 
which are referred to as high yield investment programs (HYIP)
Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, 
so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. 
In this paper, a novel methodology is introduced which ...