A Decentralized Identity-Based Blockchain Solution for Privacy-Preserving Licensing of Individual-Controlled Data to Prevent Unauthorized Secondary Data Usage

This paper presents a design for a blockchain solution aimed at the prevention of unauthorized secondary use of data. This solution brings together advances from the fields of identity management, confidential computing, and advanced data usage control. In the area of identity management, the solution is aligned with emerging decentralized identity standards: decentralized identifiers (DIDs), DID communication and verifiable credentials (VCs). In respect to confidential computing, the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (FHE) scheme is incorporated with the system to protect the privacy of the individual’s data and prevent unauthorized secondary use when being shared with potential users. In the area of advanced data usage control, the solution leverages the PRIV-DRM solution architecture to derive a novel approach to licensing of data usage to prevent unauthorized secondary usage of data held by individuals. Specifically, our design covers necessary roles in the data-sharing ecosystem: the issuer of personal data, the individual holder of the personal data (i.e., the data subject), a trusted data storage manager, a trusted license distributor, and the data consumer. The proof-of-concept implementation utilizes the decentralized identity framework being developed by the Hyperledger Indy/Aries project. A genomic data licensing use case is evaluated, which shows the feasibility and scalability of the solution.

Strategic Diversification for Asynchronous Asset Trading: Insights from Generalized Coherence Analysis of Cryptocurrency Price Movements

Non-linear interactions between cryptocurrency price movements can elicit cross-frequency coupling (CFC) wherein one set of frequencies in the 1st timeseries is coupled to another set of frequencies in the 2nd timeseries. To investigate this, we use a generalized coherence approach to detect and quantify both linear (i.e., iso-frequency coupling, IFC) and non-linear coherence (CFC) and the associated phase relationships between the intra-day price changes of various pairs of cryptocurrencies for the year 2020. Using this information, we further assess the risk reduction associated with diversification of portfolios between each pair of a small market capital and a large market capital cryptocurrency, for both synchronous and asynchronous trading conditions. While mean pairwise IFC values were lower for smaller cryptocurrencies, pairwise CFC values were more heterogeneous and had no correlation with the market capital size. Diversification of portfolios resulted in reduced risk for synchronously-traded pairs of those cryptocurrencies which had low IFC. For asynchronous trading conditions, if the larger market capital cryptocurrency was traded at a higher frequency, diversification almost always reduced risk. Thus, the novel approach used in this study reveals important insights into the complex dynamics that govern the price trends of cryptocurrencies.

Cryptocurrency Competition and Market Concentration in the Presence of Network Effects

When network products and services become more valuable as their userbase grows (network effects), this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the market they have also been used as a valuation tool. The recent resurgence of Bitcoin has been partly attributed to network effects, too. We study the existence of network effects in six cryptocurrencies from their inception to obtain a high-level overview of the application of network effects in the cryptocurrency market. We show that, contrary to the usual implications of network effects, they do not serve to concentrate the cryptocurrency market, nor do they accord any one cryptocurrency a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptocurrency networks, they are not (yet) a defining feature of the cryptocurrency marketas a whole.

On the Intraday Behavior of Bitcoin

We analyze the intraday time series of Bitcoin, comparing its features with those of traditional financial assets such as stocks and exchange rates. The results shed light on similarities as well as significant deviations from the standard patterns. In particular, our most interesting finding is the unusual presence of significant negative first-order autocorrelation of returns calculated on medium-frequency timeframes, such as one, two and four hours, signaling the presence of systematic mean reversion. It is also found that larger price movements lead to stronger reversals, in percentage terms. We finally point out the potential exploitability of the phenomenon by implementing a basic algorithmic trading strategy and retroactively applying it to the data. We explain the findings mainly through (i) investor and trader overreaction, (ii) excess volatility and (iii) cascading liquidations due to excessive use of leverage by market participants.

Enhancing Electronic Voting With A Dual-Blockchain Architecture

Voting is one of the most fundamental aspects of democracy. Over the past few decades, voting methods around the world have expanded from traditional paper ballot systems to electronic voting (e-voting), in which votes are written directly to computer memory. Like any computer system, voting machines are susceptible to technical vulnerabilities that open up opportunities for hackers to tamper with votes, causing the use of electronic voting technology to raise concerns about ballot security. We describe how electronic voting can be supported by blockchain technology to ensure voter secrecy, vote correctness, and equal voting rights. In this paper, we present a system using two separate blockchains, each with separate transactions and consensus algorithms. We describe a prototype implementation that validates our ideas by executing several proof-of-concept simulations of a range of voting scenarios.

Bitcoin Returns and the Frequency of Daily Abnormal Returns

This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).

New Crypto-Secured Lending System with a Two-Way Collateral Function

All existing secured loans, including crypto-secured loans, are provided under the condition that the collateral entrusted by the borrower is kept safe during the loan term. In other words, they use a one-way collateral function. Thus, a frequent drawback of these loans is that the collateral value increases if and only if the collateral price increases. To resolve this problem, this paper proposes a new crypto-secured lending system incorporating a new two-way collateral function. It would allow a borrower to invest proportions of their own collateral by predicting the market in both directions to make profits irrespective of whether the price of the collateral increases or decreases. This benefits the borrower since profit can be made even if the price of the collateral drops, by betting on the price decrease. This new lending system could include a new hedged portion, unlike traditional secured lending systems. As a result, larger loans can be made under this arrangement; further, this portion provides the advantage of reducing the underlying collateral price volatility risk.