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FairHash: A Fair and Memory/Time-efficient Hashmap

Summary: FairHash: data-dependent hashmap with group-level statistical parity across buckets, giving formal guarantees for three fairness notions. Three algorithmic families span zero/uniform unfairness vs. memory overhead, with ranking/cut/discrepancy trade-offs and near-baseline performance. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6963
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,174 | 23.34%
DOI
10.1145/3654939

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{shahbazi_sigmod24,
        title = {{FairHash: A Fair and Memory/Time-efficient Hashmap}},
        author = {Shahbazi, Nima and Sintos, Stavros and Asudeh, Abolfazl},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654939},
        url = {https://dl.acm.org/doi/10.1145/3654939},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,238 Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor 2026 SIGMOD 5.093636e-05
10,511 On Fair Epsilon Net and Geometric Hitting Set 2026 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

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