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Found 10 papers, 8 papers with code
Auto.gov: Learning-based On-chain Governance for Decentralized Finance (DeFi)
In recent years, decentralized finance (DeFi) has experienced remarkable growth, with various protocols such as lending protocols and automated market makers (AMMs) emerging.
Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Recommender systems are commonly trained on centrally collected user interaction data like views or clicks.
Liquidations: DeFi on a Knife-edge
We find that liquidators' efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated.
Revisiting Transactional Statistics of High-scalability Blockchain
In particular, 96% of the transactions on EOSIO were triggered by the airdrop of a currently valueless token; on Tezos, 76% of throughput was used for maintaining consensus; and over 94% of transactions on XRPL carried no economic value.
Cryptography and Security
Privacy-Preserving Bandits
Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations.
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
In this paper we present Percival, a browser-embedded, lightweight, deep learning-powered ad blocker.
Smart Contract Vulnerabilities: Does Anyone Care?
In the last year we have seen a great deal of both academic and practical interest in the topic of vulnerabilities in smart contracts, particularly those developed for the Ethereum blockchain.
Cryptography and Security
The Anatomy of a Cryptocurrency Pump-and-Dump Scheme
While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets.
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources.
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively.
