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DOI: 10.4230/LIPIcs.AFT.2024.27

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol

Authors: Mahsa Bastankhah, Viraj Nadkarni, Chi Jin, Sanjeev Kulkarni, and Pramod Viswanath

Published in: LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024)

Abstract

Decentralized finance (DeFi) borrowing and lending platforms are crucial to the decentralized economy, involving two main participants: lenders who provide assets for interest and borrowers who offer collateral exceeding their debt and pay interest. Collateral volatility necessitates over-collateralization to protect lenders and ensure competitive returns. Traditional DeFi platforms use a fixed interest rate curve based on the utilization rate (the fraction of available assets borrowed) and determine over-collateralization offline through simulations to manage risk. This method doesn't adapt well to dynamic market changes, such as price fluctuations and evolving user needs, often resulting in losses for lenders or borrowers. In this paper, we introduce an adaptive, data-driven protocol for DeFi borrowing and lending. Our approach includes a high-frequency controller that dynamically adjusts interest rates to maintain market stability and competitiveness with external markets. Unlike traditional protocols, which rely on user reactions and often adjust slowly, our controller uses a learning-based algorithm to quickly find optimal interest rates, reducing the opportunity cost for users during periods of misalignment with external rates. Additionally, we use a low-frequency planner that analyzes user behavior to set an optimal over-collateralization ratio, balancing risk reduction with profit maximization over the long term. This dual approach is essential for adaptive markets: the short-term component maintains market stability, preventing exploitation, while the long-term planner optimizes market parameters to enhance profitability and reduce risks. We provide theoretical guarantees on the convergence rates and adversarial robustness of the short-term component and the long-term effectiveness of our protocol. Empirical validation confirms our protocol’s theoretical benefits.

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Mahsa Bastankhah, Viraj Nadkarni, Chi Jin, Sanjeev Kulkarni, and Pramod Viswanath. Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bastankhah_et_al:LIPIcs.AFT.2024.27,
  author =	{Bastankhah, Mahsa and Nadkarni, Viraj and Jin, Chi and Kulkarni, Sanjeev and Viswanath, Pramod},
  title =	{{Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{27:1--27:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-345-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{316},
  editor =	{B\"{o}hme, Rainer and Kiffer, Lucianna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024.27},
  URN =		{urn:nbn:de:0030-drops-209634},
  doi =		{10.4230/LIPIcs.AFT.2024.27},
  annote =	{Keywords: Defi borrow-lending, adaptive market design, decentralized finance}
}

Document

DOI: 10.4230/LIPIcs.AFT.2023.3

F3B: A Low-Overhead Blockchain Architecture with Per-Transaction Front-Running Protection

Authors: Haoqian Zhang, Louis-Henri Merino, Ziyan Qu, Mahsa Bastankhah, Vero Estrada-Galiñanes, and Bryan Ford

Published in: LIPIcs, Volume 282, 5th Conference on Advances in Financial Technologies (AFT 2023)

Abstract

Front-running attacks, which benefit from advanced knowledge of pending transactions, have proliferated in the blockchain space since the emergence of decentralized finance. Front-running causes devastating losses to honest participants and continues to endanger the fairness of the ecosystem. We present Flash Freezing Flash Boys (F3B), a blockchain architecture that addresses front-running attacks by using threshold cryptography. In F3B, a user generates a symmetric key to encrypt their transaction, and once the underlying consensus layer has finalized the transaction, a decentralized secret-management committee reveals this key. F3B mitigates front-running attacks because, before the consensus group finalizes it, an adversary can no longer read the content of a transaction, thus preventing the adversary from benefiting from advanced knowledge of pending transactions. Unlike other mitigation systems, F3B properly ensures that all unfinalized transactions, even with significant delays, remain private by adopting per-transaction protection. Furthermore, F3B addresses front-running at the execution layer; thus, our solution is agnostic to the underlying consensus algorithm and compatible with existing smart contracts. We evaluated F3B on Ethereum with a modified execution layer and found only a negligible (0.026%) increase in transaction latency, specifically due to running threshold decryption with a 128-member secret-management committee after a transaction is finalized; this indicates that F3B is both practical and low-cost.

Cite as

Haoqian Zhang, Louis-Henri Merino, Ziyan Qu, Mahsa Bastankhah, Vero Estrada-Galiñanes, and Bryan Ford. F3B: A Low-Overhead Blockchain Architecture with Per-Transaction Front-Running Protection. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 3:1-3:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{zhang_et_al:LIPIcs.AFT.2023.3,
  author =	{Zhang, Haoqian and Merino, Louis-Henri and Qu, Ziyan and Bastankhah, Mahsa and Estrada-Gali\~{n}anes, Vero and Ford, Bryan},
  title =	{{F3B: A Low-Overhead Blockchain Architecture with Per-Transaction Front-Running Protection}},
  booktitle =	{5th Conference on Advances in Financial Technologies (AFT 2023)},
  pages =	{3:1--3:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-303-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{282},
  editor =	{Bonneau, Joseph and Weinberg, S. Matthew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.3},
  URN =		{urn:nbn:de:0030-drops-191921},
  doi =		{10.4230/LIPIcs.AFT.2023.3},
  annote =	{Keywords: Blockchain, DeFi, Front-running Mitigation}
}

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Documents authored by Bastankhah, Mahsa

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol

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F3B: A Low-Overhead Blockchain Architecture with Per-Transaction Front-Running Protection

Abstract

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