When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery

• URL: http://arxiv.org/abs/2412.17303v1
• Date: Mon, 23 Dec 2024 05:50:11 GMT
• Title: When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery
• Authors: Bo Jiang, Wanrong Zhang, Donghang Lu, Jian Du, Qiang Yan,
• Abstract summary: Local Differential Privacy protocols have been successfully deployed in real-world scenarios by tech companies like Google, Apple, and Microsoft. We propose a Generalized Count Mean Sketch protocol that captures many existing frequency estimation protocols. We present a novel protocol for collecting data within unknown domain, as our frequency estimation protocols only work effectively with known data domain.
• Score: 7.746385592375338
• License:
• Abstract: 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.

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