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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Peng Tang
- 2. Xiya Shao
- 3. Rui Chen
- 4. Ning Wang
- 5. Shanqing Guo
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,930 | Marginal Release Under Local Differential Privacy | 2018 | SIGMOD | 0.00010040732 |
| 2,408 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 8.8780076e-05 |
| 3,433 | LDP-IDS: Local Differential Privacy for Infinite Data Streams | 2022 | SIGMOD | 7.0998035e-05 |
| 7,485 | Differentially Private Data Generation with Missing Data | 2024 | VLDB | 4.7180617e-05 |
Previous
Page 1 / 1
Next