A study of user experiences and network analysis on anonymity and traceability of bitcoin transactions

This paper investigates the anonymity of bitcoin transactions and significance of awareness of the technology by bitcoin users, alongside their experiences in tracing transactions. Bitcoin enables users to carry out transactions anonymously with the virtual currency without unveiling where the real-world source of the income has come from. These transactions may occur without revealing locations or any personal identifiable information of the person who is sending or receiving bitcoins. While there are existing surveys which test bitcoin users’ awareness of the technology, they do not focus on bitcoin users’ own experience using the technology in terms of tracing transactions and use of anti-forensic tools to increase the level of anonymity. This paper reports significance of users’ opinions on traceability and anonymity of bitcoin transactions and compares users’ viewpoints collected from a survey with experimental findings observed using network analysis tools

The Evolution of Embedding Metadata in Blockchain Transactions

The use of blockchains is growing every day, and their utility has greatly expanded from sending and receiving crypto-coins to smart-contracts and decentralized autonomous organizations. Modern blockchains underpin a variety of applications: from designing a global identity to improving satellite connectivity. In our research we look at the ability of blockchains to store metadata in an increasing volume of transactions and with evolving focus of utilization. We further show that basic approaches to improving blockchain privacy also rely on embedding metadata. This paper identifies and classifies real-life blockchain transactions embedding metadata of a number of major protocols running essentially over the bitcoin blockchain. The empirical analysis here presents the evolution of metadata utilization in the recent years, and the discussion suggests steps towards preventing criminal use. Metadata are relevant to any blockchain, and our analysis considers primarily bitcoin as a case study. The paper concludes that simultaneously with both expanding legitimate utilization of embedded metadata and expanding blockchain functionality, the applied research on improving anonymity and security must also attempt to protect against blockchain abuse.Comment: 9 pages, 6 figures, 1 table, 2018 International Joint Conference on Neural Network

Examining and Exposing the Darknet

This thesis consists of two studies; the first study is “Diving into the Darknet” and the second is “Exposing the Darknet on Mobile Devices”. The Darknet is a network of hidden sites and services which are built based on anonymity. In “Diving into the Darknet”, we applied different data science methods to establish the relationships between the data in the data set. This data set has information related to seller, drug types, and transactions. Additionally, we used Tableau to visualize the data set. For the second study, we took a digital forensics perspective of the Darknet. Orfox and Orbot, a Browser Bundle which is used to access the Darknet through mobile devices, were installed on a Galaxy Note 5 with Android 6.0.1. After the investigation, some theories of past studies were disproved by our method combined with E3: DS, a mobile digital forensics software package by Paraben. We believe that the combination of information from a user’s point of view and a technical perspective of digital forensics would bring the Darknet to light. Through this thesis, we hope that knowledge about the Darknet will be revealed and better understood

Cloud/fog computing resource management and pricing for blockchain networks

The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.Comment: 16 pages, double-column version, accepted by IEEE Internet of Things Journa