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Found 5 papers, 3 papers with code
Props for Machine-Learning Security
We propose protected pipelines or props for short, a new approach for authenticated, privacy-preserving access to deep-web data for machine learning (ML).
SquirRL: Automating Attack Discovery on Blockchain Incentive Mechanisms with Deep Reinforcement Learning
Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol.
Cryptography and Security
Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges
We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots.
Cryptography and Security Computer Science and Game Theory
Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
Smart contracts are applications that execute on blockchains.
Cryptography and Security
Stealing Machine Learning Models via Prediction APIs
In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i. e., "steal") the model.
