In this paper, we identify a new form of attack, called the Balance attack, against proof-of-work blockchain systems. The novelty of this attack consists of delaying network communications between multiple subgroups of nodes with balanced mining power. Our theoretical analysis captures the precise tradeoff between the network delay and the mining power of the attacker needed to double spend in Ethereum with high probability. We quantify our probabilistic analysis with statistics taken from the R3 consortium, and show that a single machine needs 20 minutes to attack the consortium. Finally, we run an Ethereum private chain in a distributed system with similar settings as R3 to demonstrate the feasibility of the approach, and discuss the application of the Balance attack to Bitcoin. Our results clearly confirm that main proof-of-work blockchain protocols can be badly suited for consortium blockchains.
The attacks become harder if the external action is taken after the transaction is committed by the blockchain. Rosenfeld's attack @cite_0 consists of issuing a transaction to a merchant. The attacker then starts solo-mining a longer branch while waiting for @math blocks to be appended so that the merchant takes an external action in response to the commit. The attack success probability depends on the number @math of blocks the merchant waits before taking an external action and the attacker mining power. However, when the attacker has more mining power than the rest of the system, the attack, also called or , is guaranteed successful, regardless of the value @math . To make the attack successful when the attacker owns only a quarter of the mining power, the attacker can incentivize other miners to form a coalition @cite_32 until the coalition owns more than half of the total mining power.
@cite_0 @cite_32
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Several new services incentivize clients to compete in solving large computation tasks in exchange for financial rewards. This model of competitive distributed computation enables every user connected to the Internet to participate in a game in which he splits his computational power among a set of competing pools -- the game is called a computational power splitting game. We formally model this game and show its utility in analyzing the security of pool protocols that dictate how financial rewards are shared among the members of a pool. As a case study, we analyze the Bitcoin crypto currency which attracts computing power roughly equivalent to billions of desktop machines, over 70 of which is organized into public pools. We show that existing pool reward sharing protocols are insecure in our game-theoretic analysis under an attack strategy called the "block withholding attack". This attack is a topic of debate, initially thought to be ill-incentivized in today's pool protocols: i.e., causing a net loss to the attacker, and later argued to be always profitable. Our analysis shows that the attack is always well-incentivized in the long-run, but may not be so for a short duration. This implies that existing pool protocols are insecure, and if the attack is conducted systematically, Bitcoin pools could lose millions of dollars worth in months. The equilibrium state is a mixed strategy -- that is -- in equilibrium all clients are incentivized to probabilistically attack to maximize their payoffs rather than participate honestly. As a result, the Bitcoin network is incentivized to waste a part of its resources simply to compete. There has been increasing interest in the problem of building accurate data mining models over aggregate data, while protecting privacy at the level of individual records. One approach for this problem is to randomize the values in individual records, and only disclose the randomized values. The model is then built over the randomized data, after first compensating for the randomization (at the aggregate level). This approach is potentially vulnerable to privacy breaches: based on the distribution of the data, one may be able to learn with high confidence that some of the randomized records satisfy a specified property, even though privacy is preserved on average.In this paper, we present a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them. Unlike earlier approaches, amplification makes it is possible to guarantee limits on privacy breaches without any knowledge of the distribution of the original data. We instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution. Next, we address the problem that the amount of randomization required to avoid privacy breaches (when mining association rules) results in very long transactions. By using pseudorandom generators and carefully choosing seeds such that the desired items from the original transaction are present in the randomized transaction, we can send just the seed instead of the transaction, resulting in a dramatic drop in communication and storage cost. Finally, we define new information measures that take privacy breaches into account when quantifying the amount of privacy preserved by randomization. Bitcoin was recently introduced as a peer-to-peer electronic currency in order to facilitate transactions outside the traditional financial system. The core of Bitcoin, the Blockchain, is the history of the transactions in the system maintained by all miners as a distributed shared register. New blocks in the Blockchain contain the last transactions in the system and are added by miners after a block mining process that consists in solving a resource consuming proof-of-work (cryptographic puzzle). The reward is a motivation for mining process but also could be an incentive for attacks such as selfish mining. In this paper we propose a solution for one of the major problems in Bitcoin : selfish mining or block-withholding attack. This attack is conducted by adversarial or selfish miners in order to either earn undue rewards or waste the computational power of honest miners. Contrary to recent solutions, our solution, ZeroBlock, prevents block-withholding using a technique free of timestamp that can be forged. Moreover, we show that our solution is compliant with nodes churn. Bitcoin is without a doubt the most successful cryptocurrency in circulation today, making it an extremely valuable target for attackers. Indeed, many studies have highlighted ways to compromise one or several Bitcoin nodes. In this paper, we take a different perspective and study the effect of large-scale network-level attacks such as the ones that may be launched by Autonomous Systems (ASes). We show that attacks that are commonly believed to be hard, such as isolating 50 of the mining power, are actually within the reach of anyone with access to a BGP-enabled network and hijacking less than 900 prefixes. Once on path, AS-level adversaries can then partition the Bitcoin network or delay block propagation significantly. The key factors that enable these attacks are the extreme centralization of Bitcoin, both from a routing and a mining perspective, along with the fact that Bitcoin messages are sent unencrypted, without integrity guarantees. We demonstrate the feasibility of large-scale attacks in practice against the deployed Bitcoin software and quantify their disruptive network-wide impact. The potential damage to Bitcoin is severe. By isolating a part of the network or delaying the propagation of blocks, network-level attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of attacks such as double spending. We provide several suggestions on approaches to mitigate such attacks employing both short-term and long-term measures.