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Content:

• Identity-Based Cryptosystems
• Buy A Richard Mille Watch With Bitcoin
• We apologize for the inconvenience...
• Blockchain in surgery: are we ready for the digital revolution?
• Interface RetrievalMethod
• Blockchain technology and crypto-assets market analysis: vulnerabilities and risk assessment
• Cryptocurrency Terms to Know Before You Invest: A Beginner’s Guide
• 1 XBT to MYR - Convert Bitcoins to Malaysian Ringgits
• ATO turns its attention to crypto

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Identity-Based Cryptosystems

Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy.

We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration.

We also draw a parallel between the crypto economies and real-world economies. To adequately address these two points, we devise a generic econometric analysis schema for cryptocurrencies. Our analysis reports that, despite the heavy emphasis on decentralization in cryptocurrencies, the wealth distribution remains in-line with the real-world economies, with the exception of Dash.

This suggests that the free-market fundamentalism doctrine may be inadequate in countering wealth inequality within a crypto-economic context: Algorithmically driven free-market implementation of these cryptocurrencies may eventually lead to wealth inequality similar to those observed in real-world economies. Economic freedom is one of the foundational pillars of the crypto-anarchist movement Ludlow, Crypto anarchism is a political ideology that focuses on using cryptographic methods to attain anonymity, freedom of speech, and freedom of trade May, often through a counter-economic environment.

A counter-economic environment facilitates financial transactions beyond the purview of a government, leading to freedom of trade London, , where a counter economy includes the free market, the black market, and the underground economy. These crypto-anarchist objectives are materialized primarily through recent developments in cryptography, privacy-focused distributed networks, and decentralized peer-to-peer currencies Chohan, , where their appeal is as an alternative to traditional financial system in that they embody increased freedom to trade DeVries, The adoption of trade-friendly regulations has been suggested to improve wealth distribution by encouraging the flow of wealth among nations Bank, ; Irwin, This article explores that line of reasoning, evaluating the hypothesis that wealth distribution improves in the absence of restrictive trade regulation, in a cryptocurrencies context, using measures of wealth concentration.

This is a contentious hypothesis because according to the inequality model developed by Boghosian , the free market model adopted by cryptocurrencies is not without limitations in this regard, suggesting that wealth naturally trickles up in a free market economy leading to wealth inequality.

In contrast, many cryptocurrency researchers have suggested that blockchain might provide a solution to the issue of wealth inequality in a free market-based economy Chohan, ; van den Hoven et al. For instance, Othman et al. However, it must be acknowledged that participation in these crypto economies is subjected to many barriers to entry, such as internet access requirement and high transaction fee.

Major cryptocurrencies tend to maintain an open distributed ledger of all financial transactions executed to date. This transparent nature of cryptocurrencies can be used to measure wealth concentration in these cryptocurrencies. Thus, this research work assesses the following question:. Past reports such as Griffin and Shams have suggested that manipulation of exchange rates through wealth concentration is feasible and has been observed in the cryptocurrency market.

According to Sai et al. This potential for successfully executing security attacks due to large wealth concentration makes it essential to understand the current state of wealth distribution. The exact implementation of a cryptocurrency-based financial system can vary significantly in different cryptocurrency implementations.

Thus, this fairer wealth distribution hypothesis needs to be assessed for a range of cryptocurrencies to increase the generality of the findings. This paper will conduct an empirical evaluation of wealth concentration in 8 major cryptocurrencies in two broad categories: Bitcoin-like 6 cryptocurrencies including Bitcoin and Ethereum-like 2 cryptocurrencies including Ethereum.

Bitcoin is currently the largest cryptocurrency by market capitalization, with a current valuation of USD Billion CoinMarketCap, Many prominent cryptocurrencies are based on the fundamental design of Bitcoin by forking copying the source code of Bitcoin Neudecker and Hartenstein, We refer to these cryptocurrencies collectively as Bitcoin-like cryptocurrencies.

For our empirical review, we shortlist the top six Bitcoin-like cryptocurrencies including Bitcoin itself based on the market capitalization: Bitcoin, Litecoin, Bitcoin Cash, Dash, ZCash, and DogeCoin. The second category of cryptocurrencies selected for the analysis is Ethereum-like cryptocurrencies.

Ethereum currently has a total market capitalization of USD Billion CoinMarketCap, , is ranked as the second-highest valued crypto asset and allows for transactions to contain transactional logic in the form of Turing complete contracts. Ethereum is also an interesting case study for wealth inequality analysis as Ethereum has a provision to allow users to write smart contracts to dictate economic behavior over the cryptocurrency in the form of a crypto token 1 Buterin et al.

Similar to Bitcoin forks, Ethereum also has several forks; among these, the most prominent example is Ethereum Classic. We review both Ethereum and Ethereum Classic for our study. We also review the current January state of wealth distribution in the top five tokens issued on the Ethereum platform for our analysis.

We conduct an econometric analysis by calculating macroeconomic measures of inequality for these cryptocurrencies and contrasting these measures with traditional economies.

We also examine an extrinsic factor, policy changes, to understand if factors outside the cryptocurrencies may influence the wealth distribution in the crypto economies. We also perform econometric analysis on the top five tokens deployed on the Ethereum platform, which helps us to understand the impact of policy configurability on wealth distribution as these tokens allow programmers to define the economic policies that govern these assets.

This methodology considers the volume, velocity, and variety of data generated by different forms of cryptocurrencies. Specifically, it reports on the potential relationship between the type of policy changes and the wealth concentration Section 4. In addition, based on our reflections on the empirical protocol adopted, the paper proposes a set of reverse engineering techniques that can be used by future researchers in their analysis of wealth concentration to partially circumvent cryptocurrency privacy provisions Section 6.

We also specifically report on how the current state of econometrics analysis in cryptocurrencies is insufficient to capture the economic aspects of these complicated assets Section 6. Economic inequality can be broadly categorized into income and wealth inequality Simpson, Income inequality examines the distribution of income in a country or political union of nations.

The notion of income inequality does not directly translate to crypto economies as the open ledger maintained by these crypto economies only contains information relevant to the wealth determined by units of currencies owned by each participant. Wealth inequality examines the economic heterogeneity of a country or a political union Cagetti and De Nardi, The exact definition of wealth varies depending on the application area; however, wealth is generally defined in terms of financial assets Hamilton and Hepburn, A financial asset is defined as a non-physical or physical asset that can be used for financial transactions Moles and Terry, Then wealth inequality is measured based on the distribution of these financial assets over a population.

However, calculating wealth inequality is harder than income inequality as individuals can have negative wealth due to financial liabilities such as credit and loans. Current statistics from Alvaredo et al. A standard method for calculating wealth inequality can be obtained through econometrics. The broad field of econometrics is concerned with applying statistical techniques to economic data to produce empirical evidence for the financial construct under examination Stock and Watson, Such measures of statistical dispersion 2 are commonly used for quantifying the wealth inequality in economies.

In , Max Lorenz developed a graphical way of representing economic inequality through the use of Lorenz curve Gastwirth, The Lorenz curve graphically represents the percentage of wealth accumulated by various portions of the population ordered by the size of their wealth Gastwirth, On the x -axis, we plot the percentage of the population, and on the y -axis, we plot the percentage of wealth.

As an illustrative example, we have plotted the Lorenz curve for Ireland based on the data obtained from CSO, for This line illustrated by the blue line in Figure 1 represent the perfect distribution of wealth. The area between the line of equality and the Lorenz curve can be used to understand the spread of inequality.

An important statistical construct used to numerically describe this spread of wealth is the Gini coefficient. The Gini coefficient is a numeric value aimed at quantifying the inequality in the distribution Gini, To calculate the Gini value for Ireland in , we use the Lorenz curve.

We can calculate the Gini Coefficient as follows:. Following this approach, we report that the Gini value for Ireland in for wealth distribution is 0. Based on Eq. Similarly, a Gini value of 0 would represent the perfect distribution of wealth in the country, i.

Thus, the Gini value calculated for Ireland 0. Thus far, we have discussed the meaning and measurement of wealth inequality in the context of world economies. In the following subsection, we review wealth inequality in a crypto-economic context.

Considering cryptocurrencies as financial assets is a topic of much debate in the economic and financial research domain Corbet et al. This is primarily driven by the argument regarding the intrinsic and extrinsic values associated with the crypto assets. For this article, we focus on the extrinsic value of cryptocurrencies by using their exchange rate to USD as a proxy. The use of USD as a proxy allows us to better draw parallels between crypto economies and traditional world economies.

Due to the open ledger nature of cryptocurrencies, it is easy to gain a macroeconomic view of the economy by conducting data analysis over the open ledgers. Most cryptocurrencies maintain a publically accessible ledger of all transactions in their financial system.

This allows us to use data analytics to construct a macro view of these cryptocurrencies. Gini coefficient has been suggested as a useful metric for measuring economic centralization in cryptocurrencies Kondor et al. Both Bitcoin and Etheruem employ different data structures to maintain records of transactions. Thus the deanonymizing process varies significantly depending on the type of blockchain under analysis.

UTXO specifies the value and state 3 of each Bitcoin present in the ecosystem. This list is then used to calculate the balance for the given address. The process of calculating balance is considerably simplified in Ethereum-like cryptocurrencies. Ethereums transaction data structure contains a balance field that can store and retrieve balance for a given address.

Determining the balance of all addresses is fundamental to the calculation of wealth distribution in cryptocurrencies. However, gaining a macro perspective is not sufficient to observe the wealth distribution in these cryptocurrencies. As indicated in Section 1, cryptocurrencies adhere to the crypto-anarchist ideology by employing privacy-preserving policies to maintain anonymity while retaining the freedom to trade.

This is primarily achieved through the use of cryptology in constructing and executing transactions. A macro view of the crypto economy without explicit consideration of this privacy-preserving nature would likely yield an inaccurate measure for wealth distribution as identifying wealth associated with individuals is difficult. That is, major cryptocurrencies, including Bitcoin and Ethereum, provide pseudo-anonymity to the users through cryptographically generated addresses.

Most of these cryptocurrencies also offer provisions for generating a new address for each transaction Gutoski and Stebila, This induces further complexity into the determination of wealth distribution as a single user in a cryptocurrency may have his wealth distributed over multiple addresses.

To avoid skewing the econometric analysis due to many addresses with a very small balance, Srinivasan and Lee propose using a monetary lower bound on balance. For instance, introducing a requirement of a minimum balance of USD for inclusion in Gini calculation can significantly improve accuracy by eliminating several addresses with very low or zero balances.

They justify this choice by arguing that many addresses in these cryptocurrencies are only used once for privacy reasons, and addresses with a low balance are unlikely to see future transactions for example addresses employed for one transaction only.

Despite or maybe because of this tweak, it is hard to establish the accuracy of this method. Srinivasan and Lee suggest using an alternate metric to measure wealth, and other forms of distribution in cryptocurrencies. For example, many prevalent cryptocurrencies are subjected to an honest majority assumption.

Buy A Richard Mille Watch With Bitcoin

The firm said the sharp rise in money laundering activity in was not surprising. The firm said the sharp rise in money laundering activity in was not surprising, given the significant growth of both legitimate and illegal crypto activity last year. Money laundering refers to that process of disguising the origin of illegally obtained money by transferring it to legitimate businesses. Mining pools, high-risk exchanges, and mixers also saw substantial increases in value received from illicit addresses, the report said. Mixers typically combine potentially identifiable or tainted cryptocurrency funds with others, so as to conceal the trail to the fund's original source.

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Are funds incorporating cryptocurrencies an inevitable next step? Will growing government intervention be positive or negative for the adoption of cryptofunds? And how are private banks responding to this trend? Join us to look back at the broader market movements of cryptocurrencies, as APB brings different market sentiments and viewpoints to the table to discuss the future role of cryptocurrencies in portfolio construction — particularly in Asia Pacific wealth management — and the ultimate appetite for cryptofunds from investors and their advisors. At OSL, Colm focuses on providing licensed global financial institutions with digital asset platforms to support the distribution of digital assets to their customers. Read More. In his role at the FATF, David is responsible for leading the FATF Secretariat in bringing to bear the combined expertise of governments around the world to fight money laundering, the financing of terrorism and the proliferation of weapons of mass destruction. This includes work to monitor how money is being laundered and terrorist organisations are raising and accessing funds; to develop global standards, best practice and guidance to mitigate new and emerging risks; and to assess the action taken by governments. Prior to his current role in Singapore, he was managing director of Bordier International in London for 10 years.

Blockchain in surgery: are we ready for the digital revolution?

Here's What Investors Should Know. Ethereum Just Hit a 6-Month Low. Upgrade Bitcoin Rewards Card: 1. There Are Thousands of Different Altcoins. Ryan Haar is a former personal finance reporter for NextAdvisor.

Interface RetrievalMethod

Luno, t he first Securities Commission-approved digital asset exchange in Malaysia, announced today that the company has processed over RM million worth of transactions in Malaysia since its relaunch in The data excludes trading volume from unregulated crypto exchanges which are also relatively popular among Malaysian crypto investors. Investments in cryptocurrency have been increasing steadily in Malaysia with many investors looking to cryptocurrencies as a good store of value or a start to their investing journey. He also noted that it was interesting to see that majority of activities on its platform comes from customers who are aged between 30 — 49 years old, contrary the belief of some who thinks that crypto investors are primarily Gen-Zs and young millennials. With the backing of DCG, the company hopes to fulfill its vision of upgrading the world to a better financial system.

Blockchain technology and crypto-assets market analysis: vulnerabilities and risk assessment

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Cryptocurrency Terms to Know Before You Invest: A Beginner’s Guide

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1 XBT to MYR - Convert Bitcoins to Malaysian Ringgits

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ATO turns its attention to crypto

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Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy. We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration. We also draw a parallel between the crypto economies and real-world economies.