Scaling Verifiable Computation Using Efficient Set Accumulators

Verifiable outsourcing systems offload a large computation to a remote server, but require that the remote server provide a succinct proof, called a SNARK, that proves that the server carried out the computation correctly. Real-world applications of this approach can be found in several blockchain systems that employ verifiable outsourcing to process a large number of transactions off-chain. This reduces the on-chain work to simply verifying a succinct proof that transaction processing was done correctly. In practice, verifiable outsourcing of state updates is done by updating the leaves of a Merkle tree, recomputing the resulting Merkle root, and proving using a SNARK that the state update was done correctly. In this work, we use a combination of existing and novel techniques to implement an RSA accumulator inside of a SNARK, and use it as a replacement for a Merkle tree. We specifically optimize the accumulator for compatibility with SNARKs. Our experiments show that the resulting system reduces costs compared to existing approaches that use Merkle trees for committing to the current state. These results apply broadly to any system that needs to offload batches of state updates to an untrusted server

Towards Solving the Blockchain Trilemma: An Exploration of Zero-Knowledge Proofs

Research on blockchain has found that the technology is no silver bullet compared to traditional data structures due to limitations regarding decentralization, security, and scalability. These limitations are summarized in the blockchain trilemma, which today represents the greatest barrier to blockchain adoption and applicability. To address these limitations, recent advancements by blockchain businesses have focused on a new cryptographic technique called Zero-knowledge proofs . While these primitives have been around for some time and despite their potential significance on blockchains, not much is known in information systems research about them and their potential effects. Therefore, we employ a multivocal literature review to explore this new tool and find that although it has the potential to resolve the trilemma, it currently only solves it in certain dimensions, which necessitates further attention and research