A major approach to overcoming the performance and scalability limitations of current blockchain protocols is to use sharding which is to split the overheads of processing transactions among multiple, smaller groups of nodes. These groups work in parallel to maximize performance while requiring significantly smaller communication, computation, and storage per node, allowing the system to scale to large networks. However, existing sharding-based blockchain protocols still require a linear amount of communication (in the number of participants) per transaction, and hence, attain only partially the potential benefits of sharding. We show that this introduces a major bottleneck to the throughput and latency of these protocols. Aside from the limited scalability, these protocols achieve weak security guarantees due to either a small fault resiliency (e.g., 1/8 and 1/4) or high failure probability, or they rely on strong assumptions (e.g., trusted setup) that limit their applicability to mainstream payment systems. We propose RapidChain, the first sharding-based public blockchain protocol that is resilient to Byzantine faults from up to a 1/3 fraction of its participants, and achieves complete sharding of the communication, computation, and storage overhead of processing transactions without assuming any trusted setup. Rapid-Chain employs an optimal intra-committee consensus algorithm that can achieve very high throughputs via block pipelining, a novel gossiping protocol for large blocks, and a provably-secure reconfiguration mechanism to ensure robustness. Using an efficient cross-shard transaction verification technique, our protocol avoids gossiping transactions to the entire network. Our empirical evaluations suggest that RapidChain can process (and confirm) more than 7,300 tx/sec with an expected confirmation latency of roughly 8.7 seconds in a network of 4,000 nodes with an overwhelming time-to-failure of more than 4,500 years. CCS CONCEPTS • Security and privacy → Distributed systems security;

No abstract

Alice and Bob want to run a protocol over a noisy channel, where a certain number of bits are flipped adversarially. Several results take a protocol requiring L bits of noise-free communication and make it robust over such a channel. In a recent breakthrough result, Haeupler described an algorithm that sends a number of bits that is conjectured to be near optimal in such a model. However, his algorithm critically requires a priori knowledge of the number of bits that will be flipped by the adversary.We describe an algorithm requiring no such knowledge. If an adversary flips T bits, our algorithm sends L + O L(T + 1) log L + T bits in expectation and succeeds with high probability in L. It does so without any a priori knowledge of T . Assuming a conjectured lower bound by Haeupler, our result is optimal up to logarithmic factors.Our algorithm critically relies on the assumption of a private channel. We show that privacy is necessary when the amount of noise is unknown.

The point of adversarial analysis is to model the worst-case performance of an algorithm. Unfortunately, this analysis may not always reect performance in practice because the adversarial assumption can be overly pessimistic. In such cases, several techniques have been developed to provide a more refined understanding of how an algorithm performs e.g., competitive analysis, parameterized analysis, and the theory of approximation algorithms. Here, we describe an analogous technique called resource competitiveness, tailored for distributed systems. Often there is an operational cost for adversarial behavior arising from bandwidth usage, computational power, energy limitations, etc. Modeling this cost provides some notion of how much disruption the adversary can inict on the system. In parameterizing by this cost, we can design an algorithm with the following guarantee: if the adversary pays T, then the additional cost of the algorithm is some function of T. Resource competitiveness yields results pertaining to secure, fault tolerant, and efficient distributed computation. We summarize these results and highlight future challenges where we expect this algorithmic tool to provide new insights.

We describe an asynchronous algorithm to solve secure multiparty computation (MPC) over n players, when strictly less than a 1/8 fraction of the players are controlled by a static adversary. For any function f that can be computed by a circuit with m gates, our algorithm requires each player to send a number of bits and perform an amount of computation that isÕ( n+m n + √ n). This significantly improves over traditional algorithms, which require each player to both send a number of messages and perform computation that is Ω(nm).