Research Pulse #52 02/14/22

  1. NFT Wash Trading Quantifying suspicious behaviour in NFT markets
    Authors: Victor von Wachter, Johannes Rude Jensen, Ferdinand Regner, and Omri Ross

The smart contract-based markets for non-fungible tokens (NFTs) on the Ethereum blockchain have seen tremendous growth in 2021, with trading volumes peaking at $3.5b in September 2021. This dramatic surge has led to industry observers questioning the authenticity of on-chain volumes, given the absence of identity requirements and the ease with which agents can control multiple addresses. We examine potentially illicit trading patterns in the NFT markets from January 2018 to mid-November 2021, gathering data from the 52 largest collections by volume. Our findings indicate that within our sample 3.93% of addresses, processing a total of 2.04% of sale transactions, trigger suspicions of market abuse. Flagged transactions contaminate nearly all collections and may have inflated the authentic trading volumes by as much as $149,5m for the period. Most flagged transaction patterns alternate between a few addresses, indicating a predisposition for manual trading. We submit that the results presented here may serve as a viable lower bound estimate for NFT wash trading on Ethereum. Even so, we argue that wash trading may be less common than what industry observers have previously estimated. We contribute to the emerging discourse on the identification and deterrence of market abuse in the cryptocurrency markets.


  1. Eliminating Sandwich Attacks with the Help of Game Theory
    Authors: Lioba Heimbach and Roger Wattenhofer

Predatory trading bots lurking in Ethereum’s mempool present invisible taxation of traders on automated market makers (AMMs). AMM traders specify a slippage tolerance to indicate the maximum price movement they are willing to accept. This way, traders avoid automatic transaction failure in case of small price movements before their trade request executes. However, while a too-small slippage tolerance may lead to trade failures, a too-large tolerance allows predatory trading bots to profit from sandwich attacks. These bots can extract the difference between the slippage tolerance and the actual price movement as profit.
In this work, we introduce the sandwich game to analyze sandwich attacks analytically from both the attacker and victim perspectives. Moreover, we provide a simple and highly effective algorithm that traders can use to set the slippage. We unveil that the vast majority of broadcast transactions can avoid sandwich attacks while simultaneously only experiencing a low risk of transaction failure. Thereby, we demonstrate that a constant auto-slippage cannot adjust to varying trade sizes and pool characteristics. Our algorithm outperforms the constant auto-slippage suggested by the biggest AMM, Uniswap, in all performed tests. Specifically, our algorithm repeatedly demonstrates a cost reduction exceeding a factor of 100.


  1. CryptoMaze: Privacy-Preserving Splitting of Off-Chain Payments
    Authors: Subhra Mazumdar and Sushmita Ruj

Payment Channel Networks or PCNs solve the problem of scalability in Blockchain by executing payments off-chain. Due to a lack of sufficient capacity in the network, high-valued payments are split and routed via multiple paths. Existing multi-path payment protocols either fail to achieve atomicity or are susceptible to wormhole attack. We propose a secure and privacy-preserving atomic multi-path payment protocol CryptoMaze. Our protocol avoids the formation of multiple off-chain contracts on edges shared by the paths routing partial payments. It also guarantees unlinkability between partial payments. We provide a formal definition of the protocol in the Universal Composability framework and analyze the security. We implement CryptoMaze on several instances of Lightning Network and simulated networks. Our protocol requires 11s for routing a payment of 0.04 BTC on a network instance comprising 25600 nodes. The communication cost is less than 1MB in the worst-case. On comparing the performance of CryptoMaze with several state-of-the-art payment protocols, we observed that our protocol outperforms the rest in terms of computational cost and has a feasible communication overhead.

Link: CryptoMaze: Privacy-Preserving Splitting of Off-Chain Payments | IEEE Journals & Magazine | IEEE Xplore

  1. HECO: Automatic Code Optimizations for Efficient Fully Homomorphic Encryption
    Authors: Alexander Viand, Patrick Jattke, Miro Haller, and Anwar Hithnawi

In recent years, Fully Homomorphic Encryption (FHE) has undergone several breakthroughs and advancements leading to a leap in performance. Today, performance is no longer a major barrier to adoption. Instead, it is the complexity of developing an efficient FHE application that currently limits deploying FHE in practice and at scale. Several FHE compilers have emerged recently to ease FHE development. However, none of these answer how to automatically transform imperative programs to secure and efficient FHE implementations. This is a fundamental issue that needs to be addressed before we can realistically expect broader use of FHE. Automating these transformations is challenging because the restrictive set of operations in FHE and their non-intuitive performance characteristics require programs to be drastically transformed to achieve efficiency. In addition, existing tools are monolithic and focus on individual optimizations. Therefore, they fail to fully address the needs of end-to-end FHE development. In this paper, we present HECO, a new end-to-end design for FHE compilers that takes high-level imperative programs and emits efficient and secure FHE implementations. In our design, we take a broader view of FHE development, extending the scope of optimizations beyond the cryptographic challenges existing tools focus on.


  1. Blockchain Smart Contract Virtual Machine Optimization Technology for Domain Name Systems
    Authors: Shiyu Wang, Keyuan Wang, Linpeng Jia, Lei Yu, Zhongcheng Li, Yi Sun, Jun Li, and Jihua Zhou

The existing domain name system relies heavily on the root domain name server and has unilateral management problems, which is not conducive to the open and equal development of the Internet environment. Blockchain technology provides new technical means to solve the centralization problem of the root resolution system but induces high consumption and slow query issues. In this paper, we studied optimizing the blockchain smart contract virtual machine, specifically EVM (Ethereum Virtual Machine), for realizing a domain name system through a smart contract. We abstracted the key data structure of domain name management and added core attributes to the account structure to accelerate domain name management through transactions. Furthermore, we modified the EVM structure and added EVM opcodes that can directly call the underlying logic related to upper-level domain name management to simplify the transaction processing flow. The evaluations show that the system has achieved a two orders of magnitude improvement in the performance of the domain name resolution system based on blockchain.

Link: Blockchain Smart Contract Virtual Machine Optimization Technology for Domain Name Systems | IEEE Conference Publication | IEEE Xplore


Research Pulse Issue #52 is out!

In NFT Wash Trading: Quantifying suspicious behaviour in NFT markets, the authors provide an analysis of NFT sales and apply a methodology to identify transactions that suggest market manipulation. They find that 3.93% of NFT addresses (2.04% of sale transactions) performed suspicious actions that might be indicative of wash trading.

In Eliminating Sandwich Attacks with the Help of Game Theory, the authors evaluate the issue of Sandwich Attacks in Ethereum. They employ behavior modeling and make of game theory to devise an algorithm to estimate price slippage. Then, they perform various tests to demonstrate that this algorithm entails a cost reduction for users which exceed a factor of 100.