Enhancing Local Differential Privacy Accuracy by Exploiting Inherent Uncertainty
Summary: Introduces inherent uncertainty to quantify correlation-induced protection from non-sensitive attributes, then uses it to calibrate LDP noise more tightly under the same sensitive-attribute \u03b5-LDP guarantee. Also proposes a dual-phase mechanism for mixed attributes, improving utility without weakening inference protection. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Peng Tang (Shandong University)
- 2. Xiya Shao (Shandong University)
- 3. Rui Chen (Harbin Engineering University)
- 4. Ning Wang (Guangdong University of Technology)
- 5. Shanqing Guo (Shandong University)
BibTeX Citation
@inproceedings{tang_sigmod26,
title = {{Enhancing Local Differential Privacy Accuracy by Exploiting Inherent Uncertainty}},
author = {Tang, Peng and Shao, Xiya and Chen, Rui and Wang, Ning and Guo, Shanqing},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786647},
url = {https://dl.acm.org/doi/10.1145/3786647},
year = {2026}
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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 |
|---|---|---|---|---|
| 1,889 | Marginal Release Under Local Differential Privacy | 2018 | SIGMOD | 9.4242478e-05 |
| 2,804 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 7.9817412e-05 |
| 3,237 | LDP-IDS: Local Differential Privacy for Infinite Data Streams | 2022 | SIGMOD | 7.4995676e-05 |
| 8,085 | Differentially Private Data Generation with Missing Data | 2024 | VLDB | 5.3917406e-05 |
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