When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910759625162752 |
|---|---|
| author | Jiang, Bo Zhang, Wanrong Lu, Donghang Du, Jian Yan, Qiang |
| author_facet | Jiang, Bo Zhang, Wanrong Lu, Donghang Du, Jian Yan, Qiang |
| contents | Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols have been successfully deployed in real-world scenarios by major tech companies like Google, Apple, and Microsoft. In this paper, we propose a Generalized Count Mean Sketch (GCMS) protocol that captures many existing frequency estimation protocols. Our method significantly improves the three-way trade-offs between communication, privacy, and accuracy. We also introduce a general utility analysis framework that enables optimizing parameter designs. {Based on that, we propose an Optimal Count Mean Sketch (OCMS) framework that minimizes the variance for collecting items with targeted frequencies.} Moreover, we present a novel protocol for collecting data within unknown domain, as our frequency estimation protocols only work effectively with known data domain. Leveraging the stability-based histogram technique alongside the Encryption-Shuffling-Analysis (ESA) framework, our approach employs an auxiliary server to construct histograms without accessing original data messages. This protocol achieves accuracy akin to the central DP model while offering local-like privacy guarantees and substantially lowering computational costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17303 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery Jiang, Bo Zhang, Wanrong Lu, Donghang Du, Jian Yan, Qiang Cryptography and Security Databases Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols have been successfully deployed in real-world scenarios by major tech companies like Google, Apple, and Microsoft. In this paper, we propose a Generalized Count Mean Sketch (GCMS) protocol that captures many existing frequency estimation protocols. Our method significantly improves the three-way trade-offs between communication, privacy, and accuracy. We also introduce a general utility analysis framework that enables optimizing parameter designs. {Based on that, we propose an Optimal Count Mean Sketch (OCMS) framework that minimizes the variance for collecting items with targeted frequencies.} Moreover, we present a novel protocol for collecting data within unknown domain, as our frequency estimation protocols only work effectively with known data domain. Leveraging the stability-based histogram technique alongside the Encryption-Shuffling-Analysis (ESA) framework, our approach employs an auxiliary server to construct histograms without accessing original data messages. This protocol achieves accuracy akin to the central DP model while offering local-like privacy guarantees and substantially lowering computational costs. |
| title | When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery |
| topic | Cryptography and Security Databases |
| url | https://arxiv.org/abs/2412.17303 |