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Documents authored by Viswanath, Pramod

Document
DOI: 10.4230/LIPIcs.AFT.2024.3
CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols

Authors: Weizhao Tang, Peiyao Sheng, Ronghao Ni, Pronoy Roy, Xuechao Wang, Giulia Fanti, and Pramod Viswanath

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

Abstract
Crash fault tolerant (CFT) consensus algorithms are commonly used in scenarios where system components are trusted - e.g., enterprise settings and government infrastructure. However, CFT consensus can be broken by even a single corrupt node. A desirable property in the face of such potential Byzantine faults is accountability: if a corrupt node breaks the protocol and affects consensus safety, it should be possible to identify the culpable components with cryptographic integrity from the node states. Today, the best-known protocol for providing accountability to CFT protocols is called PeerReview; it essentially records a signed transcript of all messages sent during the CFT protocol. Because PeerReview is agnostic to the underlying CFT protocol, it incurs high communication and storage overhead. We propose CFT-Forensics, an accountability framework for CFT protocols. We show that for a special family of forensics-compliant CFT protocols (which includes widely-used CFT protocols like Raft and multi-Paxos), CFT-Forensics gives provable accountability guarantees. Under realistic deployment settings, we show theoretically that CFT-Forensics operates at a fraction of the cost of PeerReview. We subsequently instantiate CFT-Forensics for Raft, and implement Raft-Forensics as an extension to the popular nuRaft library. In extensive experiments, we demonstrate that Raft-Forensics adds low overhead to vanilla Raft. With 256 byte messages, Raft-Forensics achieves a peak throughput 87.8% of vanilla Raft at 46% higher latency (+44 ms). We finally integrate Raft-Forensics into the open-source central bank digital currency OpenCBDC, and show that in wide-area network experiments, Raft-Forensics achieves 97.8% of the throughput of Raft, with 14.5% higher latency (+326 ms).
Cite as

Weizhao Tang, Peiyao Sheng, Ronghao Ni, Pronoy Roy, Xuechao Wang, Giulia Fanti, and Pramod Viswanath. CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 3:1-3:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{tang_et_al:LIPIcs.AFT.2024.3,
  author =	{Tang, Weizhao and Sheng, Peiyao and Ni, Ronghao and Roy, Pronoy and Wang, Xuechao and Fanti, Giulia and Viswanath, Pramod},
  title =	{{CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{3:1--3:25},
  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.2024.3},
  URN =		{urn:nbn:de:0030-drops-209399},
  doi =		{10.4230/LIPIcs.AFT.2024.3},
  annote =	{Keywords: CFT Protocols, forensics, blockchain}
}
Document
DOI: 10.4230/LIPIcs.AFT.2024.5
Proof of Diligence: Cryptoeconomic Security for Rollups

Authors: Peiyao Sheng, Ranvir Rana, Senthil Bala, Himanshu Tyagi, and Pramod Viswanath

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

Abstract
Layer 1 (L1) blockchains such as Ethereum are secured under an "honest supermajority of stake" assumption for a large pool of validators who verify each and every transaction on it. This high security comes at a scalability cost which not only effects the throughput of the blockchain but also results in high gas fees for executing transactions on chain. The most successful solution for this problem is provided by optimistic rollups, Layer 2 (L2) blockchains that execute transactions outside L1 but post the transaction data on L1. The security for such L2 chains is argued, informally, under the assumption that a set of nodes will check the transaction data posted on L1 and raise an alarm (a fraud proof) if faulty transactions are detected. However, all current deployments lack a proper incentive mechanism for ensuring that these nodes will do their job "diligently", and simply rely on a cursory incentive alignment argument for security. We solve this problem by introducing an incentivized watchtower network designed to serve as the first line of defense for rollups. Our main contribution is a "Proof of Diligence" protocol that requires watchtowers to continuously provide a proof that they have verified L2 assertions and get rewarded for the same. Proof of Diligence protocol includes a carefully-designed incentive mechanism that is provably secure when watchtowers are rational actors, under a mild rational independence assumption. Our proposed system is now live on Ethereum testnet. We deployed a watchtower network and implemented Proof of Diligence for multiple optimistic rollups. We extract execution as well as inclusion proofs for transactions as a part of the bounty. Each watchtower has minimal additional computational overhead beyond access to standard L1 and L2 RPC nodes. Our watchtower network comprises of 10 different (rationally independent) EigenLayer operators, secured using restaked Ethereum and spread across three different continents, watching two different optimistic rollups for Ethereum, providing them a decentralized and trustfree first line of defense. The watchtower network can be configured to watch the batches committed by sequencer on L1, providing an approximately 3 minute (cryptoeconomically secure) finality since the additional overhead for watching is very low. This is much lower than the finality delay in the current setup where it takes about 45 minutes for state assertions on L1, and hence will not delay the finality process on L1.
Cite as

Peiyao Sheng, Ranvir Rana, Senthil Bala, Himanshu Tyagi, and Pramod Viswanath. Proof of Diligence: Cryptoeconomic Security for Rollups. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 5:1-5:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{sheng_et_al:LIPIcs.AFT.2024.5,
  author =	{Sheng, Peiyao and Rana, Ranvir and Bala, Senthil and Tyagi, Himanshu and Viswanath, Pramod},
  title =	{{Proof of Diligence: Cryptoeconomic Security for Rollups}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{5:1--5:24},
  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.2024.5},
  URN =		{urn:nbn:de:0030-drops-209417},
  doi =		{10.4230/LIPIcs.AFT.2024.5},
  annote =	{Keywords: blockchain, rollup, game theory, security}
}
Document
DOI: 10.4230/LIPIcs.AFT.2024.25
Adaptive Curves for Optimally Efficient Market Making

Authors: Viraj Nadkarni, Sanjeev Kulkarni, and Pramod Viswanath

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

Abstract
Automated Market Makers (AMMs) are essential in Decentralized Finance (DeFi) as they match liquidity supply with demand. They function through liquidity providers (LPs) who deposit assets into liquidity pools. However, the asset trading prices in these pools often trail behind those in more dynamic, centralized exchanges, leading to potential arbitrage losses for LPs. This issue is tackled by adapting market maker bonding curves to trader behavior, based on the classical market microstructure model of Glosten and Milgrom. Our approach ensures a zero-profit condition for the market maker’s prices. We derive the differential equation that an optimal adaptive curve should follow to minimize arbitrage losses while remaining competitive. Solutions to this optimality equation are obtained for standard Gaussian and Lognormal price models using Kalman filtering. A key feature of our method is its ability to estimate the external market price without relying on price or loss oracles. We also provide an equivalent differential equation for the implied dynamics of canonical static bonding curves and establish conditions for their optimality. Our algorithms demonstrate robustness to changing market conditions and adversarial perturbations, and we offer an on-chain implementation using Uniswap v4 alongside off-chain AI co-processors.
Cite as

Viraj Nadkarni, Sanjeev Kulkarni, and Pramod Viswanath. Adaptive Curves for Optimally Efficient Market Making. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 25:1-25:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{nadkarni_et_al:LIPIcs.AFT.2024.25,
  author =	{Nadkarni, Viraj and Kulkarni, Sanjeev and Viswanath, Pramod},
  title =	{{Adaptive Curves for Optimally Efficient Market Making}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{25:1--25:22},
  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.2024.25},
  URN =		{urn:nbn:de:0030-drops-209612},
  doi =		{10.4230/LIPIcs.AFT.2024.25},
  annote =	{Keywords: Automated market makers, Adaptive, Glosten-Milgrom, Decentralized Finance}
}
Document
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.
Cite as

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}
}
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