Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. Even systems with strong theoretical security guarantees in traditional settings, where users are either Byzantine or honest, often exclude analysis of rational users, who may exploit incentives to deviate from honest behavior. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested.In this work, we propose SquirRL, a framework for using deep reinforcement learning to identify attack strategies on blockchain incentive mechanisms. With minimal setup, SquirRL replicates known theoretical results on the Bitcoin protocol. In more complex and realistic settings, as when mining power varies over time, it identifies attack strategies superior to those known in the literature. Finally, SquirRL yields results suggesting that classical selfish mining attacks against Bitcoin lose effectiveness in the presence of multiple attackers. These results shed light on why selfish mining, which is unobserved to date in the wild, may be a poor attack strategy.

We propose Tesseract, a secure real-time cryptocurrency exchange service. Existing centralized exchange designs are vulnerable to theft of funds, while decentralized exchanges cannot offer real-time cross-chain trades. All currently deployed exchanges are also vulnerable to frontrunning attacks. Tesseract overcomes these flaws and achieves a best-of-both-worlds design by using a trusted execution environment. The task of committing the recent trade data to independent cryptocurrency systems presents an all-or-nothing fairness problem, to which we present ideal theoretical solutions, as well as practical solutions. Tesseract supports not only real-time cross-chain cryptocurrency trades, but also secure tokenization of assets pegged to cryptocurrencies. For instance, Tesseract-tokenized bitcoins can circulate on the Ethereum blockchain for use in smart contracts. We provide a demo implementation of Tesseract that supports Bitcoin, Ethereum, and similar cryptocurrencies.

Proof-of-work (PoW) cryptocurrency blockchains like Bitcoin secure vast amounts of money. Their operators, called miners, expend resources to generate blocks and receive monetary rewards for their effort. Blockchains are, in principle, attractive targets for Denial-of-Service (DoS) attacks: There is fierce competition among coins, as well as potential gains from short selling. Classical DoS attacks, however, typically target a few servers and cannot scale to systems with many nodes. There have been no successful DoS attacks to date against prominent cryptocurrencies.We present Blockchain DoS (BDoS), the first incentive-based DoS attack that targets PoW cryptocurrencies. Unlike classical DoS, BDoS targets the system's mechanism design: It exploits the reward mechanism to discourage miner participation. Previous DoS attacks against PoW blockchains require an adversary's mining power to match that of all other miners. In contrast, BDoS can cause a blockchain to grind to a halt with significantly less resources, e.g., 17% as of Feb 2019 in Bitcoin according to our empirical study.BDoS differs from known attacks like Selfish Mining in its aim not to increase an adversary's revenue, but to disrupt the system. Although it bears some algorithmic similarity to those attacks, it introduces a new adversarial model, goals, algorithm, and gametheoretic analysis. Beyond its direct implications for operational blockchains, BDoS introduces the novel idea that an adversary can manipulate miners' incentives by proving the existence of a secret longest chain without actually publishing blocks.

Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested. In this work, we propose SquirRL, a framework for using deep reinforcement learning to analyze attacks on blockchain incentive mechanisms. We demonstrate SquirRL's power by first recovering known attacks: (1) the optimal selfish mining attack in Bitcoin [56], and (2) the Nash equilibrium in block withholding attacks [18]. We also use SquirRL to obtain several novel empirical results. First, we discover a counterintuitive flaw in the widely used rushing adversary model when applied to multi-agent Markov games with incomplete information. Second, we demonstrate that the optimal selfish mining strategy identified in [56] is actually not a Nash equilibrium in the multi-agent selfish mining setting. In fact, our results suggest (but do not prove) that when more than two competing agents engage in selfish mining, there is no profitable Nash equilibrium. This is consistent with the lack of observed selfish mining in the wild. Third, we find a novel attack on a simplified version of Ethereum's finalization mechanism, Casper the Friendly Finality Gadget (FFG) that allows a strategic agent to amplify her rewards by up to 30%. Notably, [12] shows that honest voting is a Nash equilibrium in Casper FFG; our attack shows that when Casper FFG is composed with selfish mining, this is no longer the case. Altogether, our experiments demonstrate SquirRL's flexibility and promise as a framework for studying attack settings that have thus far eluded theoretical and empirical understanding.

Blockchains and more general distributed ledgers are becoming increasingly popular as efficient, reliable, and persistent records of data and transactions. Unfortunately, they ensure reliability and correctness by making all data public, raising confidentiality concerns that eliminate many potential uses.In this paper we present Solidus, a protocol for confidential transactions on public blockchains, such as those required for asset transfers with on-chain settlement. Solidus operates in a framework based on real-world financial institutions: a modest number of banks each maintain a large number of user accounts. Within this framework, Solidus hides both transaction values and the transaction graph (i.e., the identities of transacting entities) while maintaining the public verifiability that makes blockchains so appealing. To achieve strong confidentiality of this kind, we introduce the concept of a Publicly-Verifiable Oblivious RAM Machine (PVORM). We present a set of formal security definitions for both PVORM and Solidus and show that our constructions are secure. Finally, we implement Solidus and present a set of benchmarks indicating that the system is efficient in practice. CCS CONCEPTS• Security and privacy → Domain-specific security and privacy architectures;