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)
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Authors
- 1. Nima Shahbazi
- 2. Stavros Sintos
- 3. Abolfazl Asudeh
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,223 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 4.1945683e-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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