Privacy Loss of Noise Perturbation via Concentration Analysis of A Product Measure
Summary: Novel geometric PLRV analysis for spherically symmetric noise via a radius–direction product measure, yielding closed-form DP moment bounds. Under the same (eps,delta)[0m-DP, it beats Gaussian noise in high dimensions and improves output/objective/gradient perturbation for ERM. (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. Shuainan Liu
- 2. Tianxi Ji
- 3. Zhongshuo Fang
- 4. Lu Wei
- 5. Pan Li
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 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,567 | PrivBasis: Frequent Itemset Mining with Differential Privacy | 2012 | VLDB | 0.0001133268 |
| 3,638 | Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics | 2017 | SIGMOD | 6.8952488e-05 |
| 4,754 | Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism | 2021 | VLDB | 5.9468785e-05 |
Previous
Page 1 / 1
Next