Towards Better Bounds for Finding Quasi-Identifiers *
Summary: Improves bounds for epsilon-separation keys (quasi-identifiers): Θ(m/√ε) uniform-sampling suffices, beating Motwani–Xu’s Θ(m/ε) baseline and speeding discovery. Provides tight uniform-sampling limits and sketching costs: strong failure requires Θ(√(m/ε)) samples; constant δ needs Ω(√(log m/ε)); coordinate-sketching uses Ω(mk log(1/ε)) space, vs Θ(mk/(αε^2)) with sampling. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ryan Hildebrant (University of California Irvine)
- 2. Quoc-Tung Le (CNRS; Claude Bernard Lyon 1 University; Ecole Normale Superieure de Lyon; INRIA; University of Lyon)
- 3. Duy-Hoang Ta (National University of Singapore)
- 4. Hoa T. Vu (San Diego State University)
BibTeX Citation
@inproceedings{hildebrant_pods23,
address = {New York, NY, USA},
series = {{PODS} '23},
title = {{Towards Better Bounds for Finding Quasi-Identifiers *}},
url = {https://dl.acm.org/doi/10.1145/3584372.3588668},
doi = {10.1145/3584372.3588668},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Hildebrant, Ryan and Le, Quoc-Tung and Ta, Duy-Hoang and Vu, Hoa T.},
year = {2023}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 161 | Robust and Efficient Fuzzy Match for Online Data Cleaning | 2003 | SIGMOD | 0.00027718195 |
| 307 | Eliminating Fuzzy Duplicates in Data Warehouses | 2002 | VLDB | 0.00021499031 |
| 8,085 | Subspace Exploration: Bounds on Projected Frequency Estimation | 2021 | PODS | 5.3942942e-05 |
| 8,094 | Space Lower Bounds for Itemset Frequency Sketches | 2016 | PODS | 5.3942942e-05 |
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