dorsal/arxiv
View SchemaNoise Reduction for Pufferfish Privacy: A Practical Noise Calibration Method
| Authors | Wenjin Yang, Ni Ding, Zijian Zhang, Jing Sun, Zhen Li, Yan Wu, Jiahang Sun, Haotian Lin, Yong Liu, Jincheng An, Liehuang Zhu |
|---|---|
| Categories | |
| ArXiv ID | 2601.06385vv1 |
| URL | https://arxiv.org/abs/2601.06385 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
This paper introduces a relaxed noise calibration method to enhance data utility while attaining pufferfish privacy. This work builds on the existing $1$-Wasserstein (Kantorovich) mechanism by alleviating the existing overly strict condition that leads to excessive noise, and proposes a practical mechanism design algorithm as a general solution. We prove that a strict noise reduction by our approach always exists compared to $1$-Wasserstein mechanism for all privacy budgets $\epsilon$ and prior beliefs, and the noise reduction (also represents improvement on data utility) gains increase significantly for low privacy budget situations--which are commonly seen in real-world deployments. We also analyze the variation and optimality of the noise reduction with different prior distributions. Moreover, all the properties of the noise reduction still exist in the worst-case $1$-Wasserstein mechanism we introduced, when the additive noise is largest. We further show that the worst-case $1$-Wasserstein mechanism is equivalent to the $\ell_1$-sensitivity method. Experimental results on three real-world datasets demonstrate $47\%$ to $87\%$ improvement in data utility.
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"date_created": "2026-02-17T05:53:08.555000Z",
"date_modified": "2026-02-17T05:53:08.555000Z",
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"record": {
"abstract": "This paper introduces a relaxed noise calibration method to enhance data utility while attaining pufferfish privacy. This work builds on the existing $1$-Wasserstein (Kantorovich) mechanism by alleviating the existing overly strict condition that leads to excessive noise, and proposes a practical mechanism design algorithm as a general solution. We prove that a strict noise reduction by our approach always exists compared to $1$-Wasserstein mechanism for all privacy budgets $\\epsilon$ and prior beliefs, and the noise reduction (also represents improvement on data utility) gains increase significantly for low privacy budget situations--which are commonly seen in real-world deployments. We also analyze the variation and optimality of the noise reduction with different prior distributions. Moreover, all the properties of the noise reduction still exist in the worst-case $1$-Wasserstein mechanism we introduced, when the additive noise is largest. We further show that the worst-case $1$-Wasserstein mechanism is equivalent to the $\\ell_1$-sensitivity method. Experimental results on three real-world datasets demonstrate $47\\%$ to $87\\%$ improvement in data utility.",
"arxiv_id": "2601.06385",
"authors": [
"Wenjin Yang",
"Ni Ding",
"Zijian Zhang",
"Jing Sun",
"Zhen Li",
"Yan Wu",
"Jiahang Sun",
"Haotian Lin",
"Yong Liu",
"Jincheng An",
"Liehuang Zhu"
],
"categories": [
"cs.CR"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Noise Reduction for Pufferfish Privacy: A Practical Noise Calibration Method",
"url": "https://arxiv.org/abs/2601.06385",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
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