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Dakai Kang

dblp:339/7333 · DBLP profile ↗
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5ranked-venue papers
3as first author
5since 2021 · last 2026
0000-0002-7867-3681ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 3 first-author · 3 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
4 papers
Distributed systems · 100%
Network and information security
2 papers
Blockchain and cryptocurrency security · 100%

Topics — the 12 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems
consensus
2.532025
FairDAG: Consensus Fairness over Multi-Proposer Causal Design · Proc. VLDB Endow. 2025
HotStuff-1: Linear Consensus with One-Phase Speculation · Proc. ACM Manag. Data 2025
SpotLess: Concurrent Rotational Consensus Made Practical Through Rapid View Synchronization · ICDE 2024
Distributed systems
blockchain
1.012026
FEAT: Fair and Efficient Adversarial Transaction Ordering · PODC 2026
Distributed systems › consensus
byzantine agreement
1.012026
FEAT: Fair and Efficient Adversarial Transaction Ordering · PODC 2026
Distributed systems › consensus › byzantine agreement
order-fairness
1.012026
FEAT: Fair and Efficient Adversarial Transaction Ordering · PODC 2026
Distributed systems › consensus
transaction ordering
1.012026
FEAT: Fair and Efficient Adversarial Transaction Ordering · PODC 2026
Distributed systems › fault tolerance
byzantine fault tolerance
0.912025
HotStuff-1: Linear Consensus with One-Phase Speculation · Proc. ACM Manag. Data 2025
Distributed systems › consistency models
causal ordering
0.912025
FairDAG: Consensus Fairness over Multi-Proposer Causal Design · Proc. VLDB Endow. 2025
Distributed systems › group communication › group membership
view synchronization
0.812024
SpotLess: Concurrent Rotational Consensus Made Practical Through Rapid View Synchronization · ICDE 2024
Blockchain and cryptocurrency security
maximal extractable value
0.312026
FEAT: Fair and Efficient Adversarial Transaction Ordering · PODC 2026
Blockchain and cryptocurrency security
consensus protocol
0.212024
SpotLess: Concurrent Rotational Consensus Made Practical Through Rapid View Synchronization · ICDE 2024
Distributed systems › fault tolerance
failure recovery
0.212024
SpotLess: Concurrent Rotational Consensus Made Practical Through Rapid View Synchronization · ICDE 2024
Distributed systems
fault tolerance
0.212024
SpotLess: Concurrent Rotational Consensus Made Practical Through Rapid View Synchronization · ICDE 2024

Methods — techniques the papers use, named apart from their topics

tournament dependency graph · 2.0view-change protocol · 1.5chained consensus · 1.5speculation · 0.9slotting · 0.9
YearPublicationVenuePosition
2026 FEAT: Fair and Efficient Adversarial Transaction Ordering
abstract
Fair transaction ordering protocols that guarantee γ-Batch-Order-Fairness are a leading defense against Maximal Extractable Value (MEV) attacks in Byzantine consensus and blockchain systems. All existing such protocols build a tournament dependency graph among transactions, incurring Θ(|T|2) comparator queries per round on a transaction set T, which becomes the dominant performance bottleneck.
Tien Tuan Anh Dinh, Dakai Kang, Mohammad Sadoghi
PODC2
2025 Brief Announcement: Carry the Tail in Consensus Protocols
abstract
We present Carry-the-Tail, the first deterministic atomic broadcast protocol in partial synchrony that, after GST, simultaneously guarantees two desirable properties: (i) a constant fraction of commits are proposed by non-faulty leaders against tail-forking attacks, and (ii) optimal, worst-case quadratic communication under a cascade of faulty leaders. The solution also guarantees linear amortized communication, i.e., the steady-state is linear. Combining these two desirable properties was not simultaneously achieved previously: on one hand, prior atomic broadcast solutions achieve per-view linear word communication complexity. However, they face a significant degradation in throughput under tail-forking attack. On the other hand, existing solutions to tail-forking attacks require either quadratic communication steps or computationally-prohibitive SNARK generation. The key technical contribution is Carry, a practical drop-in mechanism for streamlined protocols in the HotStuff family. Carry guarantees good performance against tail-forking and removes most leader-induced stalls, while retaining linear traffic and protocol simplicity. Carry-the-Tail implements the Carry mechanism on HotStuff-2.
Suyash Gupta 0001, Dakai Kang, Dahlia Malkhi, Mohammad Sadoghi
DISC2
2025 HotStuff-1: Linear Consensus with One-Phase Speculation
abstract
This paper introduces HotStuff-1, a BFT consensus protocol that improves the latency of HotStuff-1 by two network hops while maintaining linear communication complexity against faults. Furthermore, HotStuff-1 incorporates an incentive-compatible leader rotation design that motivates leaders to propose transactions promptly. HotStuff-1 achieves a reduction of two network hops by speculatively sending clients early finality confirmations, after one phase of the protocol. Introducing speculation into streamlined protocols is challenging because, unlike stable-leader protocols, these protocols cannot stop the consensus and recover from failures. Thus, we identify prefix speculation dilemma in the context of streamlined protocols; HotStuff-1 is the first streamlined protocol to resolve it. HotStuff-1 embodies an additional mechanism, slotting , that thwarts delays caused by (1) rationally-incentivized leaders and (2) malicious leaders inclined to sabotage others' progress. The slotting mechanism allows leaders to dynamically drive as many decisions as allowed by network transmission delays before view timers expire, thus mitigating both threats.
Dakai Kang, Suyash Gupta 0001, Dahlia Malkhi, Mohammad Sadoghi
Proc. ACM Manag. Data1
2025 FairDAG: Consensus Fairness over Multi-Proposer Causal Design
Dakai Kang, Junchao Chen 0003, Anh Dinh, Mohammad Sadoghi
Proc. VLDB Endow.1
2024 SpotLess: Concurrent Rotational Consensus Made Practical Through Rapid View Synchronization
abstract
The emergence of blockchain technology has renewed the interest in consensus-based data management systems that are resilient to failures. To maximize the throughput of these systems, we have recently seen several prototype consensus solutions that optimize for throughput at the expense of overall implementation complexity, high costs, and reliability. Due to this, it remains unclear how these prototypes will perform in real-world environments. In this paper, we present SpotLess, a novel concurrent rotational consensus protocol made practical. Central to SpotLess is the combination of (1) a chained rotational consensus design for replicating requests with a reduced message cost and low-cost failure recovery that eliminates the traditional complex, error-prone view-change protocol; (2) the novel Rapid View Synchronization protocol that enables SpotLess to work in more general network assumptions, without a need for a Global Synchronization Time to synchronize view, and recover valid earlier views with the aid of non-faulty replicas without the need to rely on the primary; (3) a high-performance concurrent consensus architecture in which independent instances of the chained consensus operate concurrently to process requests with high throughput, thereby avoiding the bottlenecks seen in other rotational protocols. Due to the concurrent consensus architecture, SpotLess greatly outperforms traditional primary-backup consensus protocols such as Pbft (by up to 430%), Narwhal-HS (by up to 137%), and HotStuff (by up to 3803%). Due to its reduced message cost, SpotLess is even able to outperform RCC, a state-of-the-art high-throughput concurrent consensus protocol, by up to 23%. Furthermore, SpotLess is able to maintain a stable and low latency and consistently high throughput even during failures.
Dakai Kang, Sajjad Rahnama, Jelle Hellings, Mohammad Sadoghi
ICDE1