Evaluating and enhancing the robustness of memory-bounded LDP protocols for streaming data

Tian Yuechi; Peng Jinyu; Li Xiaoguang; Zhang Qianlong; Li Fenghua; Zuo Jinxin; Niu Ben

doi:10.11959/j.issn.1000-436x.TXXB260318

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Evaluating and enhancing the robustness of memory-bounded LDP protocols for streaming data

Papers | 更新时间：2026-09-09

- Evaluating and enhancing the robustness of memory-bounded LDP protocols for streaming data
- Journal on Communications Vol. 47, Issue 8, Pages: 60-77(2026)
- 作者机构：
  
  1.中国科学院信息工程研究所，北京 100085
  2.中国科学院大学网络空间安全学院，北京 100049
  3.网络空间安全防御全国重点实验室，北京 100085
  4.西安电子科技大学网络与信息安全学院，陕西西安 710126
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62332018;62402363);Shaanxi Province Postdoctoral Research Funding Program(2024BSHSDZZ106)
- DOI：10.11959/j.issn.1000-436x.TXXB260318
  CLC： TN92
- Received：03 June 2026，
  
  Revised：2026-07-06，
  
  Accepted：06 July 2026，
  
  Published：25 August 2026
- 稿件说明：
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田月池,彭锦钰,李效光等.数据流场景下内存受限本地差分隐私协议鲁棒性评估与增强[J].通信学报,2026,47(08):60-77.

Tian Yuechi,Peng Jinyu,Li Xiaoguang,et al.Evaluating and enhancing the robustness of memory-bounded LDP protocols for streaming data[J].Journal on Communications,2026,47(08):60-77.
田月池,彭锦钰,李效光等.数据流场景下内存受限本地差分隐私协议鲁棒性评估与增强[J].通信学报,2026,47(08):60-77. DOI： 10.11959/j.issn.1000-436x.TXXB260318.

Tian Yuechi,Peng Jinyu,Li Xiaoguang,et al.Evaluating and enhancing the robustness of memory-bounded LDP protocols for streaming data[J].Journal on Communications,2026,47(08):60-77. DOI： 10.11959/j.issn.1000-436x.TXXB260318.

摘要

本地差分隐私（LDP）因其本地扰动的特性易受数据投毒攻击，现有研究多聚焦于静态数据协议，对内存受限场景下采用域压缩技术的流式数据采集分析协议鲁棒性研究不足。针对BGR、DSR、BDR、CNR这4种主流LDP流式高频项识别协议展开研究。首先，提出一种攻击驱动的鲁棒性评估框架，设计基于最大化频率提升的数据投毒攻击，通过分析协议对该攻击的抵抗性来评估协议的鲁棒性。其次，将攻击成功率（ASR）作为评估指标，在3个现实数据集上评估发现DSR协议鲁棒性最高；同时，发现存储空间大小依然对协议鲁棒性有显著影响。最后，提出一种基于分布一致性的攻击检测方案，不依赖具体协议设计，通过数据频率重建技术解决受限内存下全域频率缺失的问题。实验表明，该方案在低隐私预算或高投毒比例下检测效果显著。

Abstract

Local differential privacy (LDP) is vulnerable to data poisoning attacks

yet the robustness of memory-bounded streaming data protocols with domain compression remains understudies. Four mainstream LDP streaming frequent item identification protocols (BGR

DSR

BDR

and CNR) were investigated. Firstly

an attack-driven evaluation framework was proposed

and a data poisoning attack maximizing frequency gain was designed. Secondly

using attack success rate (ASR) as the evaluation metric

experiments on three datasets showed that DSR was the most robust protocol

and storage size significantly impacts robustness. Finally

a protocol-agnostic attack detection scheme based on distribution consistency was proposed

utilizing data frequency reconstruction to address missing global frequencies. Experimental results demonstrate that the proposed scheme achieves significant detection performance under low privacy budgets or high poisoning ratios.

关键词

Keywords

references

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