Validated Byzantine Asynchronous Multidimensional Approximate Agreement

Consider an asynchronous system where each node begins with some point in $\mathbb{R}^m$. Given some fixed $\epsilon > 0$, we wish to have every nonfaulty node eventually output a point in $\mathbb{R}^m$, where all outputs are within distance $\epsilon$ of each other, and are within the convex hull of the original nonfaulty inputs. This problem, when some of the nodes are adversarial, is known as the ``Byzantine Asynchronous Multidimensional Approximate Agreement'' problem. Previous landmark work by Mendes et al. and Vaidya et al. presented two solutions to the problem. Both of these solutions require exponential computation by each node in each round. Furthermore, the work provides a lower bound showing that it is impossible to solve the task of approximate agreement if $n\leq (m+2)t$, and thus the protocols assume that $n>(m+2)t$. We present a Byzantine Asynchronous Multidimensional Approximate Agreement protocol in the validated setting of Cachin et al. Our protocol terminates after a logarithmic number of rounds, and requires only polynomial computation in each round. Furthermore, it is resilient to $t<\frac{n}{3}$ Byzantine nodes, which we prove to be optimal in the validated setting. In other words, working on the task in the validated setting allows us to significantly improve on previous works in several significant metrics. In addition, the techniques presented in this paper can easily yield a protocol in the original non-validated setting which requires exponential computation only in the first round, and polynomial computation in every subsequent round

The Vulnerable Nature of Decentralized Governance in DeFi

Decentralized Finance (DeFi) platforms are often governed by Decentralized Autonomous Organizations (DAOs) which are implemented via governance protocols. Governance tokens are distributed to users of the platform, granting them voting rights in the platform's governance protocol. Many DeFi platforms have already been subject to attacks resulting in the loss of millions of dollars in user funds. In this paper we show that governance tokens are often not used as intended and may be harmful to the security of DeFi platforms. We show that (1) users often do not use governance tokens to vote, (2) that voting rates are negatively correlated to gas prices, (3) voting is very centralized. We explore vulnerabilities in the design of DeFi platform's governance protocols and analyze different governance attacks, focusing on the transferable nature of voting rights via governance tokens. Following the movement and holdings of governance tokens, we show they are often used to perform a single action and then sold off. We present evidence of DeFi platforms using other platforms' governance protocols to promote their own agenda at the expense of the host platform

Twilight: A Differentially Private Payment Channel Network

Payment channel networks (PCNs) provide a faster and cheaper alternative to transactions recorded on the blockchain. Clients can trustlessly establish payment channels with relays by locking coins and then send signed payments that shift coin balances over the network\u27s channels. Although payments are never published, anyone can track a client\u27s payment by monitoring changes in coin balances over the network\u27s channels. We present Twilight, the first PCN that provides a rigorous differential privacy guarantee to its users. Relays in Twilight run a noisy payment processing mechanism that hides the payments they carry. This mechanism increases the relay\u27s cost, so Twilight combats selfish relays that wish to avoid it using a trusted execution environment (TEE) that ensures they follow its protocol. The TEE does not store the channel\u27s state, which minimizes the trusted computing base. Crucially, Twilight ensures that even if a relay breaks the TEE\u27s security, it cannot break the integrity of the PCN. We analyze Twilight in terms of privacy and cost and study the trade-off between them. We implement Twilight using Intel\u27s SGX framework and evaluate its performance using relays deployed on two continents. We show that a route consisting of 4 relays handles 820 payments/sec

Correlated Rounding of Multiple Uniform Matroids and Multi-Label Classification

We introduce correlated randomized dependent rounding where, given multiple points y^1,...,y^n in some polytope Psubseteq [0,1]^k, the goal is to simultaneously round each y^i to some integral z^i in P while preserving both marginal values and expected distances between the points. In addition to being a natural question in its own right, the correlated randomized dependent rounding problem is motivated by multi-label classification applications that arise in machine learning, e.g., classification of web pages, semantic tagging of images, and functional genomics. The results of this work can be summarized as follows: (1) we present an algorithm for solving the correlated randomized dependent rounding problem in uniform matroids while losing only a factor of O(log{k}) in the distances (k is the size of the ground set); (2) we introduce a novel multi-label classification problem, the metric multi-labeling problem, which captures the above applications. We present a (true) O(log{k})-approximation for the general case of metric multi-labeling and a tight 2-approximation for the special case where there is no limit on the number of labels that can be assigned to an object

Suboptimality in DeFi

The Decentralized Finance (DeFi) ecosystem has proven to be immensely popular in facilitating financial operations such as lending and exchanging assets, with Ethereum-based platforms holding a combined amount of more than 30 billion USD. The public availability of these platforms\u27 code together with real-time data on all user interactions and platform liquidity has given rise to sophisticated automatic tools that recognize profit opportunities on behalf of users and seize them. In this work, we formalize three core DeFi primitives which together are responsible for a daily volume of over 100 million USD in Ethereum-based platforms alone: (1) lending and borrowing funds, (2) liquidation of insolvent loans, and (3) using flash-swaps to close arbitrage opportunities between cryptocurrency exchanges. The profit which can be made from each primitive is then cast as an optimization problem that can be readily solved. We use our formalization to analyze several case studies for each primitive, showing that popular platforms and tools which promise to automatically optimize profits for users, actually fall short. In specific instances, the profits can be increased by more than 100%, with highest amount of ``missed\u27\u27 revenue by a single suboptimal action equal to 428.14 ETH, or roughly 517K USD. Finally, we show that many missed opportunities to make a profit do not go unnoticed by other users. Indeed, suboptimal transactions are sometimes immediately followed by ``trailing\u27\u27 back-running transactions which extract additional profits using similar actions. By analyzing a subset of such events, we uncover that some users who frequently create such trailing transactions are heavily tied to specific miners, meaning that all of their transactions appear only in blocks mined by one miner in particular. As some of the backrun non-optimal transactions are private, we hypothesize that the users who create them are, in fact, miners (or users collaborating with miners) who use inside information known only to them to make a profit, thus gaining an unfair advantage

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e.g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance