Fairness and Efficiency in DAG-based Cryptocurrencies

Bitcoin is a decentralised digital currency that serves as an alternative to existing transaction systems based on an external central authority for security. Although Bitcoin has many desirable properties, one of its fundamental shortcomings is its inability to process transactions at high rates. To address this challenge, many subsequent protocols either modify the rules of block acceptance (longest chain rule) and reward, or alter the graphical structure of the public ledger from a tree to a directed acyclic graph (DAG). Motivated by these approaches, we introduce a new general framework that captures ledger growth for a large class of DAG-based implementations. With this in hand, and by assuming honest miner behaviour, we (experimentally) explore how different DAG-based protocols perform in terms of fairness, i.e., if the block reward of a miner is proportional to their hash power, as well as efficiency, i.e. what proportion of user transactions a ledger deems valid after a certain length of time. Our results demonstrate fundamental structural limits on how well DAG-based ledger protocols cope with a high transaction load. More specifically, we show that even in a scenario where every miner on the system is honest in terms of when they publish blocks, what they point to, and what transactions each block contains, fairness and efficiency of the ledger can break down at specific hash rates if miners have differing levels of connectivity to the P2P network sustaining the protocol

Predicting personal traits from facial images using convolutional neural networks augmented with facial landmark information

We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits.Microsoft Researc

On the Unfairness of Blockchain

The success of Bitcoin largely relies on the perception of a fair underlying peer-to-peer protocol: blockchain. Fairness here essentially means that the reward (in bitcoins) given to any participant that helps maintain the consistency of the protocol by mining, is proportional to the computational power devoted by that participant to the mining task. Without such perception of fairness, honest miners might be disincentivized to maintain the protocol, leaving the space for dishonest miners to reach a majority and jeopardize the consistency of the entire system. We prove, in this paper, that blockchain is actually unfair, even in a distributed system of only two honest miners. In a realistic setting where message delivery is not instantaneous, the ratio between the (expected) number of blocks committed by two miners is at least exponential in the product of the message delay and the difference between the two miners' hashrates. To obtain our result, we model the growth of blockchain, which may be of independent interest. We also apply our result to explain recent empirical observations and vulnerabilities

Consensus from Signatures of Work

Assuming the existence of a public-key infrastructure (PKI), digital signatures are a fundamental building block in the design of secure consensus protocols with optimal resilience. More recently, with the advent of blockchain protocols like Bitcoin, consensus has been considered in the ``permissionless\u27\u27 setting where no authentication or even point-to-point communication is available. Yet, despite some positive preliminary results, there has been no attempt to formalize a building block that is sufficient for designing consensus protocols in this setting. In this work we fill this void by putting forth a formalization of such a primitive, which we call {\em signatures of work} (SoW). Distinctive features of our new notion are a lower bound on the number of steps required to produce a signature; fast verification; {\em moderate unforgeability}---producing a sequence of SoWs, for chosen messages, does not provide an advantage to an adversary in terms of running time; and signing time independence---most relevant in concurrent multi-party applications, as we show. Armed with SoW, we then present a new permissionless consensus protocol which is secure assuming an honest majority of computational power, thus providing a blockchain counterpart to the classical Dolev-Strong consensus protocol. The protocol is built on top of a SoW-based blockchain and standard properties of the underlying hash function, thus improving on the only known provably secure consensus protocol in this setting, which relies on the random-oracle model in a fundamental way

Fairness and efficiency in DAG-based cryptocurrencies

Bitcoin is a decentralised digital currency that serves as an alternative to existing transaction systems based on an external central authority for security. Although Bitcoin has many desirable properties, one of its fundamental shortcomings is its inability to process transactions at high rates. To address this challenge, many subsequent protocols either modify the rules of block acceptance (longest chain rule) and reward, or alter the graphical structure of the public ledger from a tree to a directed acyclic graph (DAG). Motivated by these approaches, we introduce a new general framework that captures ledger growth for a large class of DAG-based implementations. With this in hand, and by assuming honest miner behaviour, we (experimentally) explore how different DAG-based protocols perform in terms of fairness, as well as efficiency. To do so, we isolate different parameters of the network (such as k, the number of pointers to previous blocks) and study their effect on those performance metrics. Our results demonstrate how the DAG-based ledger protocols described by our framework cope with a high transaction load. More specifically, we show that even in a scenario where every miner on the system is honest in terms of when they publish blocks, what they point to, and what transactions each block contains, fairness and efficiency of this kind of ledgers can break down at specific hash rates if miners have differing levels of connectivity to the P2P network sustaining the protocol