Kartik Ravidas Nayak
Duke University, NC·Computer Science·Early-career
Commerce and finance usually depend on a few trusted organizations to keep records honest; if those organizations fail, the honesty of the whole system goes with them. Blockchains spread that trust across many participants, so records stay correct even when some participants act maliciously.
- Fields
Computer Science
Mathematics & Statistics
- Topics
consensus protocols
blockchain
distributed computing
- Grants
- 1 known grant, through Apr 2028
What they work on
- Define and study mechanisms for fault detection and recovery in consensus protocols, and analyze how accountability acts as a feedback loop on the security and efficiency of blockchain systems.
- Design secure, efficient self-stabilizing synchronous consensus protocols that recover on their own from corrupted states while tolerating malicious behavior by a minority of participants.
- Analyze consensus when participants are rational rather than simply honest or malicious, using game theory to determine when parties have no incentive to deviate from the protocol.
- Determine the limits of privacy in proof-of-stake protocols and design randomized privacy mechanisms that keep transactions and stake information private while preserving consensus guarantees.
More about the research
For such systems to be practical they have to tolerate a large share of bad actors, remove any incentive to cheat, identify who attacked and recover afterwards, and still move fast, send little data between participants, and keep transactions private. No existing blockchain satisfies all of these at once. This project works on the consensus protocols at the heart of such systems, the rules by which participants agree on a single shared record, and aims to close the gap between what theory guarantees and what deployed systems need.
Why it matters
Blockchains increasingly underpin financial infrastructure, from decentralized finance to proposed central bank digital currencies, where an undetected attack or a leaked transaction has real cost. Protocols that can name and recover from misbehaving participants, stay secure when parties act on incentives, and hide transaction details while scaling could make these systems safer for the public and permissioned deployments that rely on them.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ning Zhang
Washington University in St Louis, MO·Computer Science·Early-career
Provide guaranteed computing and I/O availability inside trusted execution environments despite an untrusted operating system, using the predictable timing of real-time cyber-physical systems to keep the trusted code small.
Remzi Arpaci-Dusseau
University of Wisconsin-Madison, WI·Computer Science
Characterize dependencies across blockchain computing, storage and network resources to build a foundation for classifying faults that cause transaction data loss.
Lili Su
Northeastern University, MA·Computer Science·Early-career
Build theory for federated learning that uses the statistical structure of each device's data, to explain why it succeeds in practice where optimization-only analyses predict failure.
The school
Duke University: 61 labs on PI Pages, $1.6B a year on research, 417 research doctorates a year.
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