Paper 2025/1414

Data Availability Sampling with Repair

Abstract

Data availability sampling (DAS) is an important technique to horizontally scale consensus protocols without compromising on the number of adversarial nodes that can be tolerated. DAS is on the technical roadmap of major blockchains such as Ethereum. A major challenge for DAS schemes, that has not been formally studied in the literature, is how incomplete shares can be repaired. The need for repairing data shares motivates key aspects of Ethereum's DAS-based sharding vision called "Danksharding". In this work, we make two contributions. First, we provide a new definitional framework that formalizes the notion of local repair, along with the security guarantees that a DAS scheme must provide. Second, we propose a new DAS scheme designed with efficient local repair in mind, based on locally-correctable multiplicity codes. To facilitate using these codes, we introduce a new multivariate polynomial commitment scheme that (i) supports efficient openings of partial derivatives of a committed polynomial, (ii) supports fast batch opening proof generation at many points, and (iii) has an algorithm to recompute (repair) opening proofs at a point from only a few other proofs. The proposed scheme improves upon the state-of-the-art Ethereum PeerDAS scheme, deployed in December 2025, in storage overhead, local repair bandwidth and coordination, while only slightly increasing dispersal cost and sampling bandwidth. As an additional benefit, our construction also supports efficient partial reconstruction, i.e., retrieving parts of the stored data. All of our techniques readily carry over to data availability schemes based on verifiable information dispersal (VID) as well.