DBScholar

Back to papers

Fairness Matters: A Tit-For-Tat Strategy Against Selfish Mining

Summary: Introduces KL-divergence-based unfairness for selfish mining and Tit-for-Tat block promotion, adaptively withholding blocks based on fork-derived suspicion. Solves the resulting nonconvex delay-vector problem approximately, supporting asynchronous/dynamic networks and reducing unfairness by 54.62%. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13102
Venue
VLDB
Year
2022
Pagerank
5.3058708e-05
Overall Rank
9,202 | 36.87%
DOI
10.14778/3565838.3565856

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{sun_vldb22,
        title = {{Fairness Matters: A Tit-For-Tat Strategy Against Selfish Mining}},
        author = {Sun, Weijie and Xu, Zihuan and Chen, Lei},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {13},
        pages = {4048--4061},
        doi = {10.14778/3565838.3565856},
        url = {https://doi.org/10.14778/3565838.3565856},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
5,045 Do the Rich Get Richer? Fairness Analysis for Blockchain Incentives 2021 SIGMOD 6.3879469e-05
6,580 Fluid: A Blockchain based Framework for Crowdsourcing 2019 SIGMOD 5.8364579e-05
11,667 When the Recursive Diversity Anonymity Meets the Ring Signature 2021 SIGMOD 5.093636e-05
Previous Page 1 / 1 Next

Semantically Similar Papers