Arthur Gervais

On the Security and Performance of Proof of Work Blockchains

Proof of Work (PoW) powered blockchains currently account for more than 90% of the total market capitalization of existing digital cryptocurrencies. Although the security provisions of Bitcoin have been thoroughly analysed, the security guarantees of variant (forked) PoW blockchains (which were instantiated with different parameters) have not received much attention in the literature. This opens the question whether existing security analysis of Bitcoins PoW applies to other implementations which have been instantiated with different consensus and/or network parameters. In this paper, we introduce a novel quantitative framework to analyse the security and performance implications of various consensus and network parameters of PoW blockchains. Based on our framework, we devise optimal adversarial strategies for double-spending and selfish mining while taking into account real world constraints such as network propagation, different block sizes, block generation intervals, information propagation mechanism, and the impact of eclipse attacks. Our framework therefore allows us to capture existing PoW-based deployments as well as PoW blockchain variants that are instantiated with different parameters, and to objectively compare the tradeoffs between their performance and security provisions.

Tampering with the Delivery of Blocks and Transactions in Bitcoin

Given the increasing adoption of Bitcoin, the number of transactions and the block sizes within the system are only expected to increase. To sustain its correct operation in spite of its ever-increasing use, Bitcoin implements a number of necessary optimizations and scalability measures. These measures limit the amount of information broadcast in the system to the minimum necessary. In this paper, we show that current scalability measures adopted by Bitcoin come at odds with the security of the system. More specifically, we show that an adversary can exploit these measures in order to effectively delay the propagation of transactions and blocks to specific nodes for a considerable amount of time---without causing a network partitioning in the system. Notice that this attack alters the information received by Bitcoin nodes, and modifies their views of the ledger state. Namely, we show that this allows the adversary to considerably increase its mining advantage in the network, and to double-spend transactions in spite of the current countermeasures adopted by Bitcoin. Based on our results, we propose a number of countermeasures in order to enhance the security of Bitcoin without deteriorating its scalability.

Misbehavior in Bitcoin: A Study of Double-Spending and Accountability

Bitcoin is a decentralized payment system that relies on Proof-of-Work (PoW) to resist double-spending through a distributed timestamping service. To ensure the operation and security of Bitcoin, it is essential that all transactions and their order of execution are available to all Bitcoin users. Unavoidably, in such a setting, the security of transactions comes at odds with transaction privacy. Motivated by the fact that transaction confirmation in Bitcoin requires tens of minutes, we analyze the conditions for performing successful double-spending attacks against fast payments in Bitcoin, where the time between the exchange of currency and goods is short (in the order of a minute). We show that unless new detection techniques are integrated in the Bitcoin implementation, double-spending attacks on fast payments succeed with considerable probability and can be mounted at low cost. We propose a new and lightweight countermeasure that enables the detection of double-spending attacks in fast transactions. In light of such misbehavior, accountability becomes crucial. We show that in the specific case of Bitcoin, accountability complements privacy. To illustrate this tension, we provide accountability and privacy definition for Bitcoin, and we investigate analytically and empirically the privacy and accountability provisions in Bitcoin.

On the privacy provisions of Bloom filters in lightweight bitcoin clients

Lightweight Bitcoin clients are gaining increasing adoption among Bitcoin users, owing to their reduced resource and bandwidth consumption. These clients support a simplified payment verification (SPV) mode as they are only required to download and verify a part of the block chain---thus supporting the usage of Bitcoin on constrained devices, such as smartphones. SPV clients rely on Bloom filters to receive transactions that are relevant to their local wallet. These filters embed all the Bitcoin addresses used by the SPV clients, and are outsourced to more powerful Bitcoin nodes which then only forward to those clients transactions relevant to their outsourced Bloom filters. In this paper, we explore the privacy of existing SPV clients. We show analytically and empirically that the reliance on Bloom filters within existing SPV clients leaks considerable information about the addresses of Bitcoin users. Our results show that an SPV client who uses a modest number of Bitcoin addresses (e.g., < 20) risks revealing almost all of his addresses. We also show that this information leakage is further exacerbated when users restart their SPV clients and/or when the adversary has access to more than one Bloom filter pertaining to the same SPV client. Motivated by these findings, we propose an efficient countermeasure to enhance the privacy of users which rely on SPV clients; our proposal can be directly integrated within existing SPV client implementations.

Revive: Rebalancing Off-Blockchain Payment Networks

Scaling the transaction throughput of decentralized blockchain ledgers such as Bitcoin and Ethereum has been an ongoing challenge. Two-party duplex payment channels have been designed and used as building blocks to construct linked payment networks, which allow atomic and trust-free payments between parties without exhausting the resources of the blockchain. Once a payment channel, however, is depleted (e.g., because transactions were mostly unidirectional) the channel would need to be closed and re-funded to allow for new transactions. Users are envisioned to entertain multiple payment channels with different entities, and as such, instead of refunding a channel (which incurs costly on-chain transactions), a user should be able to leverage his existing channels to rebalance a poorly funded channel. To the best of our knowledge, we present the first solution that allows an arbitrary set of users in a payment channel network to securely rebalance their channels, according to the preferences of the channel owners. Except in the case of disputes (similar to conventional payment channels), our solution does not require on-chain transactions and therefore increases the scalability of existing blockchains. In our security analysis, we show that an honest participant cannot lose any of its funds while rebalancing. We finally provide a proof of concept implementation and evaluation for the Ethereum network.

Quantifying Web Adblocker Privacy

Web advertisements, an integral part of today’s web browsing experience, financially support countless websites. Meaningful advertisements, however, require behavioral targeting, user tracking and profile fingerprinting that raise serious privacy concerns. To counter privacy issues and enhance usability, adblockers emerged as a popular way to filter web requests that do not serve the website’s main content. Despite their popularity, little work has focused on quantifying the privacy provisions of adblockers.

Quantifying Location Privacy Leakage from Transaction Prices

Large-scale datasets of consumer behavior might revolutionize the way we gain competitive advantages and increase our knowledge in the respective domains. At the same time, valuable datasets pose potential privacy risks that are difficult to foresee. In this paper we study the impact that the prices from consumers’ purchase histories have on the consumers’ location privacy. We show that using a small set of low-priced product prices from the consumers’ purchase histories, an adversary can determine the country, city, and local retail store where the transaction occurred with high confidence. Our paper demonstrates that even when the product category, precise time of purchase, and currency are removed from the consumers’ purchase history (e.g., for privacy reasons), information about the consumers’ location is leaked. The results are based on three independent datasets containing thousands of low-priced and frequently-bought consumer products. The results show the existence of location privacy risks when releasing consumer purchase histories. As such, the results highlight the need for systems that hide transaction details in consumer purchase histories.