多级本地化差分隐私算法推荐框架

王瀚仪; 李效光; 毕文卿; 陈亚虹; 李凤华; 牛犇

doi:10.11959/j.issn.1000-436x.2022106

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多级本地化差分隐私算法推荐框架

学术论文 | 更新时间：2024-06-05

- 多级本地化差分隐私算法推荐框架
- Multi-level local differential privacy algorithm recommendation framework
- 通信学报 2022年43卷第8期页码：52-64
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 中国科学院大学网络空间安全学院，北京 100049
  3. 西安电子科技大学网络与信息安全学院，陕西西安 710071
- 作者简介：
  
  [ "王瀚仪（1994- ），女，吉林省吉林市人，中国科学院信息工程研究所博士生，主要研究方向为隐私计算" ]
  [ "李效光（1995- ），男，陕西西安人，西安电子科技大学博士生，主要研究方向为差分隐私" ]
  [ "毕文卿（1997- ），女，山东菏泽人，中国科学院信息工程研究所硕士生，主要研究方向为隐私计算" ]
  [ "陈亚虹（1995- ），女，福建泉州人，中国科学院信息工程研究所博士生，主要研究方向为隐私计算" ]
  [ "李凤华（1966- ），男，湖北浠水人，博士，中国科学院信息工程研究所研究员、博士生导师，主要研究方向为网络与系统安全、信息保护、隐私计算" ]
  [ "牛犇（1984- ），男，陕西西安人，博士，中国科学院信息工程研究所副研究员、博士生导师，主要研究方向为隐私计算、网络安全防护" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2021YFB3100300);国家自然科学基金资助项目;The National Natural Science Foundation of China(61872441);国家自然科学基金资助项目;The National Natural Science Foundation of China(61932015)
- DOI：10.11959/j.issn.1000-436x.2022106
  中图分类号： TP309
- 网络出版日期：2022-08，
  
  纸质出版日期：2022-08-25
- 稿件说明：
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王瀚仪, 李效光, 毕文卿, 等. 多级本地化差分隐私算法推荐框架[J]. 通信学报, 2022,43(8):52-64.

Hanyi WANG, Xiaoguang LI, Wenqing BI, et al. Multi-level local differential privacy algorithm recommendation framework[J]. Journal on communications, 2022, 43(8): 52-64.
王瀚仪, 李效光, 毕文卿, 等. 多级本地化差分隐私算法推荐框架[J]. 通信学报, 2022,43(8):52-64. DOI： 10.11959/j.issn.1000-436x.2022106.

Hanyi WANG, Xiaoguang LI, Wenqing BI, et al. Multi-level local differential privacy algorithm recommendation framework[J]. Journal on communications, 2022, 43(8): 52-64. DOI： 10.11959/j.issn.1000-436x.2022106.

摘要

本地化差分隐私（LDP）算法通常为不同用户分配相同的保护机制及参数，却忽视了不同用户终端设备资源与隐私需求的差异。为此，提出一种多级 LDP 算法推荐框架。该框架考虑服务商以及用户的需求，通过服务商和用户的多级管理实现多用户差异化隐私保护。将框架应用至频数统计场景形成 LDP 算法推荐方案，改进LDP算法以保证统计结果的可用性，设计协同机制保护用户的隐私偏好。实验结果证明了所提方案的可用性。

Abstract

Local differential privacy (LDP) algorithm usually assigned the same protection mechanism and parameters to different users.However

it ignored the differences among the device resources and the privacy requirements of different users.For this reason

a multi-level LDP algorithm recommendation framework was proposed.The server and the users’ requirements were considered in the framework

and the multi-users’ differential privacy protections were realized by the server and the users’ multi-level management.The framework was applied to the frequency statistics scenario to form an LDP algorithm recommendation scheme.LDP algorithm was improved to ensure the availability of statistical results

and a collaborative mechanism was designed to protect users’ privacy preferences.The experimental results demonstrate the availability of the proposed scheme.

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Keywords

references

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