Measuring Database Unfairness via Dependency Quantification Under Differential Privacy
Summary: Introduces DP-computable unfairness measures satisfying positivity and monotonicity: mutual-information/TV, weighted-MaxSAT repair, and top-k tuple contributions. Provides private algorithms with sensitivity–accuracy analyses, exposing influential records and bias despite restricted access. (summarized by gpt-5.6-luna on Aug 17 2026)
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Authors
- 1. Mariia Vologdin (Hebrew University)
- 2. Yuchao Tao (Independent)
- 3. Amir Gilad (Hebrew University)
BibTeX Citation
@article{vologdin_vldb26,
title = {{Measuring Database Unfairness via Dependency Quantification Under Differential Privacy}},
author = {Vologdin, Mariia and Tao, Yuchao and Gilad, Amir},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {2411--2425},
doi = {10.14778/3819518.3819560},
url = {https://doi.org/10.14778/3819518.3819560},
year = {2026}
}
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