skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: DeFi Survival Analysis: Insights Into the Emerging Decentralized Financial Ecosystem
We propose a survival analysis approach for discovering and characterizing user behavior and risks for lending protocols in decentralized finance (DeFi). We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We illustrate our approach using transactions in Aave, one of the largest lending protocols. We develop a DeFi survival analysis pipeline that first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods modified for competing risks when appropriate, such as Kaplan–Meier survival curves, cumulative incidence functions, Cox hazard regression, and Fine-Gray models for sub-distribution hazards to gain insights into usage patterns and risks within the protocol. We show how, by varying the index and outcome events as well as covariates, we can use DeFi survival analysis to answer questions like “How does loan size affect the repayment schedule of the loan?”; “How does loan size affect the likelihood that an account gets liquidated?”; “How does user behavior vary between Aave markets?”; “How has user behavior in Aave varied from quarter to quarter?” The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions.  more » « less
Award ID(s):
2113906
PAR ID:
10600799
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Distributed Ledger Technologies: Research and Practice
Volume:
3
Issue:
1
ISSN:
2769-6480
Page Range / eLocation ID:
1 to 23
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, The International Conference on Mathematical Research for Blockchain Economy)
    Pardalos, Panos; Kotsireas, Ilias; Guo, Yike; Knottenbelt, William (Ed.)
    We propose a decentralized finance (DeFi) survival analysis approach for discovering and characterizing user behavior and risks in lending protocols. We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We demonstrate our approach using transactions in AAVE, one of the largest lending protocols. We develop a DeFi survival analysis pipeline which first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods such as median survival times, Kaplan–Meier survival curves, and Cox hazard regression to gain insights into usage patterns and risks within the protocol. We show how by varying the index and outcome events, we can utilize DeFi survival analysis to answer three different questions. What do users do after a deposit? How long until borrows are first repaid or liquidated? How does coin type influence liquidation risk? The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions. 
    more » « less
  2. ; ; ; ; ; (, The International Conference on Mathematical Research for Blockchain Economy)
    The emerging decentralized financial ecosystem (DeFi) is comprised of numerous protocols, one type being lending protocols. People make transactions in lending protocols, each of which is attributed to a specific blockchain address which could represent an externally-owned account (EOA) or a smart contract. Using Aave, one of the largest lending protocols, we summarize the transactions made by each address in each quarter from January 1, 2021, through December 31, 2022. We cluster these quarterly summaries to identify and name common patterns of quarterly behavior in Aave. We then use these clusters to glean insights into the dominant behaviors in Aave. We show that there are three kinds of keepers, i.e., a specific type of users tasked with the protocol’s governance, but only one kind of keeper finds consistent success in making profits from liquidations. We identify the largest-scale accounts in Aave and the highest-risk kinds of behavior on the platform. Additionally, we use the temporal aspect of the clusters to track how common behaviors change through time and how usage has shifted in the wake of major events that impacted the crypto market, and we show that there seem to be problems with user retention in Aave as many of the addresses that perform transactions do not remain in the market for long. 
    more » « less
  3. ; ; ; ; (, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)
    Böhme, Rainer; Kiffer, Lucianna (Ed.)
    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. 
    more » « less
  4. ; ; (, In Ninth ACM Conference on Data and Application Security and Privacy (CODASPY ’19))
    Distributed credit networks, such as Ripple [18] and Stellar [21], are becoming popular as an alternative means for financial transactions. However, the current designs do not preserve user privacy or are not truly decentralized. In this paper, we explore the creation of a distributed credit network that preserves user and transaction privacy and unlinkability. We propose BlAnC, a novel, fully decentralized blockchain-based credit network where credit transfer between a sender-receiver pair happens on demand. In BlAnC, multiple concurrent transactions can occur seamlessly, and malicious network actors that do not follow the protocols and/or disrupt operations can be identified efficiently. We perform security analysis of our proposed protocols in the universal composability framework to demonstrate its strength, and discuss how our network handles operational dynamics. We also present preliminary experiments and scalability analyses. 
    more » « less
  5. ; ; (, ACM Asia Conference on Computer and Communications Security (ACM AsiaCCS))
    The Bitcoin scalability problem has led to the development of offchain financial mechanisms such as payment channel networks (PCNs) which help users process transactions of varying amounts, including micro-payment transactions, without writing each transaction to the blockchain. Since PCNs only allow path-based transactions, effective, secure routing protocols that find a path between a sender and receiver are fundamental to PCN operations. In this paper, we propose RACED, a routing protocol that leverages the idea of Distributed Hash Tables (DHTs) to route transactions in PCNs in a fast and secure way. Our experiments on real-world transaction datasets show that RACED gives an average transaction success ratio of 98.74%, an average pathfinding time of 31.242 seconds, which is 1.65 × 103, 1.8 × 103, and 4 × 102 times faster than three other recent routing protocols that offer comparable security/privacy properties. We rigorously analyze and prove the security of RACED in the Universal Composability framework. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email