Sarisht Wadhwa (Duke University), Jannis Stoeter (Duke University), Fan Zhang (Duke University, Yale University), Kartik Nayak (Duke University)

Hashed Time-Locked Contracts (HTLCs) are a widely used primitive in blockchain systems such as payment channels, atomic swaps, etc. Unfortunately, HTLC is incentive-incompatible and is vulnerable to bribery attacks. The state-of-the-art solution is MAD-HTLC (Oakland'21), which proposes an elegant idea that leverages miners' profit-driven nature to defeat bribery attacks.

In this paper, we show that MAD-HTLC is still vulnerable as it only considers a somewhat narrow set of passive strategies by miners. Through a family of novel reverse-bribery attacks, we show concrete active strategies that miners can take to break MAD-HTLC and profit at the loss of MAD-HTLC users. For these attacks, we present their implementation and game-theoretical profitability analysis.

Based on the learnings from our attacks, we propose a new HTLC realization, He-HTLC (Our specification is lightweight and inert to incentive manipulation attacks. Hence, we call it He-HTLC where He stands for Helium.) that is provably secure against all possible strategic manipulation (passive and active). In addition to being secure in a stronger adversary model, He-HTLC achieves other desirable features such as low and user-adjustable collateral, making it more practical to implement and use the proposed schemes. We implemented He-HTLC on Bitcoin and the transaction cost of He-HTLC is comparative to average Bitcoin transaction fees.

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Nico Schiller (Ruhr-Universität Bochum), Merlin Chlosta (CISPA Helmholtz Center for Information Security), Moritz Schloegel (Ruhr-Universität Bochum), Nils Bars (Ruhr University Bochum), Thorsten Eisenhofer (Ruhr University Bochum), Tobias Scharnowski (Ruhr-University Bochum), Felix Domke (Independent), Lea Schönherr (CISPA Helmholtz Center for Information Security), Thorsten Holz (CISPA Helmholtz Center for Information Security)

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Semi-Automated Synthesis of Driving Rules

Diego Ortiz, Leilani Gilpin, Alvaro A. Cardenas (University of California, Santa Cruz)

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Investigating User Behaviour Towards Fake News on Social Media...

Yasmeen Abdrabou (University of the Bundeswehr Munich), Elisaveta Karypidou (LMU Munich), Florian Alt (University of the Bundeswehr Munich), Mariam Hassib (University of the Bundeswehr Munich)

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