Shin’ichiro Matsuo

Yoko Shibuya, Go Yamamoto, Fuhito Kojima et al.

Several attacks have been proposed against Proof-of-Work blockchains, which may increase the attacker's share of mining rewards (e.g., selfish mining, block withholding). A further impact of such attacks, which has not been considered in prior work, is that decreasing the profitability of mining for honest nodes incentivizes them to stop mining or to leave the attacked chain for a more profitable one. The departure of honest nodes exacerbates the attack and may further decrease profitability and incentivize more honest nodes to leave. In this paper, we first present an empirical analysis showing that there is a statistically significant correlation between the profitability of mining and the total hash rate, confirming that miners indeed respond to changing profitability. Second, we present a theoretical analysis showing that selfish mining under such elastic hash supply leads either to the collapse of a chain, i.e., all honest nodes leaving, or to a stable equilibrium depending on the attacker's initial share.

Kentaro Sako, Shin'ichiro Matsuo, Sachin Meier

Fairness is an important trait of open, free markets. Ethereum is a platform meant to enable digital, decentralized markets. Though many researchers debate the market's fairness, there are few discussions around the fairness of automated markets, such as those hosted on Ethereum. In this paper, using pilot studies, we consider unfair factors caused by adding the program. Because CryptoKitties is one of the major blockchain-based games and has been in operation for an extended period of time, we focus on its market to examine fairness. As a result, we concluded that a gene determination algorithm in this game has little randomness, and a significant advantage to gain profit is given to players who know its bias over those who do not. We state incompleteness and impact of the algorithm and other factors. Besides, we suppose countermeasures to reduce CryptoKitties' unfairness as a market.

Michael Bartholic, Zhengrong Gu, Jianan Su et al.

Data driven approaches to problem solving are, in many regards, the holy grail of evidence backed decision making. Using first-party empirical data to analyze behavior and establish predictions yields us the ability to base in-depth analyses on particular individuals and reduce our dependence on generalizations. Modern mobile and embedded devices provide a wealth of sensors and means for collecting and tracking individualized data. Applying these assets to the realm of insurance (which is a statistically backed endeavor at heart) is certainly nothing new; yet doing so in a way that is privacy-driven and secure has not been a central focus of implementers. Existing data-driven insurance technologies require a certain level of trust in the data tracking agency (i.e. insurer) to not misuse, mishandle, or over-collect user data. Smart contracts and blockchain technology provide us an opportunity to re-balance these systems such that the blockchain itself is a trusted agent which both insurers and the insured can confide in. We propose a "Smart Auto Insurance" system that minimizes data sharing while simultaneously providing quality-of-life improvements to both sides. Furthermore, we use a simple game theoretical argument to show that the clients using such a system are disincentivized from behaving adversarially.