Permissionless blockchains protocols such as Bitcoin are inherently limited in transaction throughput and latency. Current efforts to address this key issue focus on off-chain payment channels that can be combined in a Payment-Channel Network (PCN) to enable an unlimited number of payments without requiring to access the blockchain other than to register the initial and final capacity of each channel. While this approach paves the way for low latency and high throughput of payments, its deployment in practice raises several privacy concerns as well as technical challenges related to the inherently concurrent nature of payments that have not been sufficiently studied so far.In this work, we lay the foundations for privacy and concurrency in PCNs, presenting a formal definition in the Universal Composability framework as well as practical and provably secure solutions. In particular, we present Fulgor and Rayo. Fulgor is the first payment protocol for PCNs that provides provable privacy guarantees for PCNs and is fully compatible with the Bitcoin scripting system. However, Fulgor is a blocking protocol and therefore prone to deadlocks of concurrent payments as in currently available PCNs. Instead, Rayo is the first protocol for PCNs that enforces non-blocking progress (i.e., at least one of the concurrent payments terminates). We show through a new impossibility result that non-blocking progress necessarily comes at the cost of weaker privacy. At the core of Fulgor and Rayo is Multi-Hop HTLC, a new smart contract, compatible with the Bitcoin scripting system, that provides conditional payments while reducing running time and communication overhead with respect to previous approaches. Our performance evaluation of Fulgor and Rayo shows that a payment with 10 intermediate users takes as few as 5 seconds, thereby demonstrating their feasibility to be deployed in practice.
Tremendous growth in cryptocurrency usage is exposing the inherent scalability issues with permissionless blockchain technology. Payment-channel networks (PCNs) have emerged as the most widely deployed solution to mitigate the scalability issues, allowing the bulk of payments between two users to be carried out off-chain. Unfortunately, as reported in the literature and further demonstrated in this paper, current PCNs do not provide meaningful security and privacy guarantees [30], [40]. In this work, we study and design secure and privacypreserving PCNs. We start with a security analysis of existing PCNs, reporting a new attack that applies to all major PCNs, including the Lightning Network, and allows an attacker to steal the fees from honest intermediaries in the same payment path. We then formally define anonymous multi-hop locks (AMHLs), a novel cryptographic primitive that serves as a cornerstone for the design of secure and privacy-preserving PCNs. We present several provably secure cryptographic instantiations that make AMHLs compatible with the vast majority of cryptocurrencies. In particular, we show that (linear) homomorphic one-way functions suffice to construct AMHLs for PCNs supporting a script language (e.g., Ethereum). We also propose a construction based on ECDSA signatures that does not require scripts, thus solving a prominent open problem in the field. AMHLs constitute a generic primitive whose usefulness goes beyond multi-hop payments in a single PCN and we show how to realize atomic swaps and interoperable PCNs from this primitive. Finally, our performance evaluation on a commodity machine finds that AMHL operations can be performed in less than 100 milliseconds and require less than 500 bytes of communication overhead, even in the worst case. In fact, after acknowledging our attack, the Lightning Network developers have implemented our ECDSA-based AMHLs into their PCN. This demonstrates the practicality of our approach and its impact on the security, privacy, interoperability, and scalability of today's cryptocurrencies.
Credit networks model transitive trust (or credit) between users in a distributed environment and have recently seen a rapid increase of popularity due to their flexible design and robustness against intrusion. They serve today as a backbone of real-world IOweYou transaction settlement networks such as Ripple and Stellar, which are deployed by various banks worldwide, as well as several other systems, such as spamresistant communication protocols and Sybil-tolerant social networks. Current solutions, however, raise serious privacy concerns, as the network topology as well as the credit value of the links are made public for apparent transparency purposes and any changes are logged. In payment scenarios, for instance, this means that all transactions have to be public and everybody knows who paid what to whom. In this work, we question the necessity of a privacy-invasive transaction ledger. In particular, we present SilentWhispers, the first distributed, privacy-preserving credit network that does not require any ledger to protect the integrity of transactions. Yet, SilentWhispers guarantees integrity and privacy of link values and transactions even in the presence of distrustful users and malicious neighbors, whose misbehavior in changing link values is detected and such users can be held accountable. We formalize these properties as ideal functionalities in the universal composability framework and present a secure realization based on a novel combination of secret-sharing-based multiparty computation and digital signature chains. SilentWhispers can handle network churn, and it is efficient as demonstrated with a prototype implementation evaluated using payments data extracted from the currently deployed Ripple payment system. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
Cloud storage has rapidly become a cornerstone of many IT infrastructures, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. Putting user data in the direct control of cloud service providers, however, raises security and privacy concerns related to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. Furthermore, even if the cloud provider is trusted, users having access to outsourced files might be malicious and misbehave. These concerns are particularly serious in sensitive applications like personal health records and credit score systems.To tackle this problem, we present GORAM, a cryptographic system that protects the secrecy and integrity of outsourced data with respect to both an untrusted server and malicious clients, guarantees the anonymity and unlinkability of accesses to such data, and allows the data owner to share outsourced data with other clients, selectively granting them read and write permissions. GORAM is the first system to achieve such a wide range of security and privacy properties for outsourced storage. In the process of designing an efficient construction, we developed two new, generally applicable cryptographic schemes, namely, batched zero-knowledge proofs of shuffle and an accountability technique based on chameleon signatures, which we consider of independent interest. We implemented GORAM in Amazon Elastic Compute Cloud (EC2) and ran a performance evaluation demonstrating the scalability and efficiency of our construction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.