A Fair and Memory/Time-efficient Hashmap

TL;DR

This paper introduces FairHash, a novel data-dependent hashmap that guarantees group fairness across buckets, balancing fairness and efficiency through three algorithmic approaches, and demonstrating superior performance on real datasets.

Contribution

FairHash is the first to ensure group fairness in hashmaps with formal guarantees, offering multiple algorithms for different trade-offs and extensive empirical validation.

Findings

FairHash achieves group fairness with minimal performance overhead.

Cut-based algorithms guarantee zero unfairness regardless of data distribution.

Ranking and discrepancy-based algorithms offer flexible fairness-performance trade-offs.

Abstract

Hashmap is a fundamental data structure in computer science. There has been extensive research on constructing hashmaps that minimize the number of collisions leading to efficient lookup query time. Recently, the data-dependant approaches, construct hashmaps tailored for a target data distribution that guarantee to uniformly distribute data across different buckets and hence minimize the collisions. Still, to the best of our knowledge, none of the existing technique guarantees group fairness among different groups of items stored in the hashmap. Therefore, in this paper, we introduce FairHash, a data-dependant hashmap that guarantees uniform distribution at the group-level across hash buckets, and hence, satisfies the statistical parity notion of group fairness. We formally define, three notions of fairness and, unlike existing work, FairHash satisfies all three of them simultaneously. We propose three families of algorithms to design fair hashmaps, suitable for different settings. Our ranking-based algorithms reduce the unfairness of data-dependant hashmaps without any memory-overhead. The cut-based algorithms guarantee zero-unfairness in all cases, irrespective of how the data is distributed, but those introduce an extra memory-overhead. Last but not least, the discrepancy-based algorithms enable trading off between various fairness notions. In addition to the theoretical analysis, we perform extensive experiments to evaluate the efficiency and efficacy of our algorithms on real datasets. Our results verify the superiority of FairHash compared to the other baselines on fairness at almost no performance cost.