arXiv subjects

Deepak Maram

Publications and source records attributed to Deepak Maram.

3 recordsLinked to original sources

arXiv2026

Guppy: Efficient Light Clients via Recursive Zero-Knowledge Proofs

Traditional light clients rely on validators committing to the entire blockchain state at every block via a state commitment such as a Merkle tree, allowing clients to verify facts using short proofs. However, maintaining large and ever-growing state trees imposes a significant burden on validators and lies on the critical path of block production. As a result, many modern high-throughput chains avoid this approach altogether. This work asks whether efficient inclusion proofs can be supported without requiring validators to maintain full state commitments. We present Guppy, a protocol that achieves this by having validators commit to just the state updates. An off-chain, untrusted service, secured by recursive Zero-Knowledge Proofs (ZKPs), then maintains a verifiable Merkle tree over the full state. This design keeps validator overhead negligible and does not increase the asymptotic complexity of block construction. Our design rests on two key technical ideas. First, a hash-chain commitment moves validator signature verification out of the ZK circuit, keeping the proving circuit efficient. Second, we design a parallel recursive proving pipeline that leverages cheap recursion in modern ZKPs to ensure latency grows only logarithmically with throughput. Our Plonky2-based implementation demonstrates that Guppy can maintain a Merkle tree of size 2^30 while processing thousands of updates per second, adding only 2-4 s of latency.

cs.CR↗

arXiv2024

zkLogin: Privacy-Preserving Blockchain Authentication with Existing Credentials

For many users, a private key based wallet serves as the primary entry point to blockchains. Commonly recommended wallet authentication methods, such as mnemonics or hardware wallets, can be cumbersome. This difficulty in user onboarding has significantly hindered the adoption of blockchain-based applications. We develop zkLogin, a novel technique that leverages identity tokens issued by popular platforms (any OpenID Connect enabled platform e.g., Google, Facebook, etc.) to authenticate transactions. At the heart of zkLogin lies a signature scheme allowing the signer to sign using their existing OpenID accounts and nothing else. This improves the user experience significantly as users do not need to remember a new secret and can reuse their existing accounts. zkLogin provides strong security and privacy guarantees. Unlike prior works, zkLogin's security relies solely on the underlying platform's authentication mechanism without the need for any additional trusted parties (e.g., trusted hardware or oracles). As the name suggests, zkLogin leverages zero-knowledge proofs (ZKP) to ensure that the sensitive link between a user's off-chain and on-chain identities is hidden, even from the platform itself. zkLogin enables a number of important applications outside blockchains. It allows billions of users to produce \textit{verifiable digital content leveraging their existing digital identities}, e.g., email address. For example, a journalist can use zkLogin to sign a news article with their email address, allowing verification of the article's authorship by any party. We have implemented and deployed zkLogin on the Sui blockchain as an additional alternative to traditional digital signature-based addresses.

cs.CR↗

arXiv2019

SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning

SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning to learn optimal join orders on the fly, during the execution of the current query. To that purpose, we divide the execution of a query into many small time slices. Different join orders are tried in different time slices. We merge result tuples generated according to different join orders until a complete result is obtained. By measuring execution progress per time slice, we identify promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We compare expected execution cost against execution cost for an optimal join order. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB's performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark and TPC-H variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice.

cs.DB↗