Eugenio Nerio Nemmi

Edoardo Marangone, Eugenio Nerio Nemmi, Daniele Friolo et al.

Decision support systems are increasingly adopted to automate decision-making processes across industries, organizations, and governments. Decision support demands data privacy, integrity, and availability while ensuring customization, security, and verifiability of the decision process. Existing solutions fail to guarantee those properties altogether. To overcome this limitation, we propose SPARTA, an approach based on Trusted Execution Environments (TEEs) that automates decision processes. To guarantee privacy, integrity, and availability, SPARTA employs efficient cryptographic techniques on notarized data with access mediated through user-defined access policies. Our solution allows users to define decision rules, which are translated to certified software objects deployed within TEEs, thereby guaranteeing customization, verifiability, and security of the process. With experiments run on public benchmarks and synthetic data, we show our approach is scalable and adds limited overhead compared to non-cryptographically secured solutions.

Eugenio Nerio Nemmi, Tobias Lauinger, Paz Grimberg et al.

Centralized cryptocurrency exchanges (CEXes) enable fast off-chain conversions between hundreds of coins. It is an open question which algorithmic trading patterns occur on these platforms. A major challenge to measuring CEXes is that their public trade data does not contain addresses or trader identifiers allowing linkage. We propose a novel methodology to infer one-way arbitrage (OWA) trading in anonymized spot trade data from CEXes. We identify 402 M likely OWA sequences in 5 years of trading on Binance (and almost 2 M during 9 years on Kraken), accounting for 0.94 % and 0.13 % of the total traded volume, respectively. While we estimate total profits of $31.2 M on Binance and $975 k on Kraken, profits from individual OWA sequences are less than $1 on average after accounting for trading fees. We also observe that OWA has become faster over time, while the profitability of individual sequences has decreased. Our findings highlight that pricing discrepancies regularly occur in CEXes, and raise questions for future work to identify the precise circumstances that enable profitable OWA.