隐私保护的群体感知数据交易算法

张勇; 李丹丹; 韩璐; 黄小红

doi:10.11959/j.issn.1000-436x.2022082

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隐私保护的群体感知数据交易算法

学术论文 | 更新时间：2024-06-05

- 隐私保护的群体感知数据交易算法
- Privacy-protected crowd-sensed data trading algorithm
- 通信学报 2022年43卷第5期页码：1-13
- 作者机构：
  
  北京邮电大学计算机学院（国家示范性软件学院），北京 100876
- 作者简介：
  
  [ "张勇（1990- ），男，河北衡水人，北京邮电大学博士生，主要研究方向为区块链、大数据交易和数据隐私保护等" ]
  [ "李丹丹（1987- ），女，河南平顶山人，博士，北京邮电大学讲师，主要研究方向为网络安全、密码学" ]
  [ "韩璐（1991- ），女，蒙古族，内蒙古赤峰人，北京邮电大学博士生，主要研究方向为安全多方计算、联邦学习等" ]
  [ "黄小红（1979- ），女，广东广州人，博士，北京邮电大学教授，主要研究方向为计算机网络应用、下一代互联网和网络安全等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2020YFE0200500);北京邮电大学优秀博士生创新基金资助项目(CX2019212)
- DOI：10.11959/j.issn.1000-436x.2022082
  中图分类号： TP399
- 网络出版日期：2022-05，
  
  纸质出版日期：2022-05-25
- 稿件说明：
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张勇, 李丹丹, 韩璐, 等. 隐私保护的群体感知数据交易算法[J]. 通信学报, 2022,43(5):1-13.

Yong ZHANG, Dandan LI, Lu HAN, et al. Privacy-protected crowd-sensed data trading algorithm[J]. Journal on communications, 2022, 43(5): 1-13.
张勇, 李丹丹, 韩璐, 等. 隐私保护的群体感知数据交易算法[J]. 通信学报, 2022,43(5):1-13. DOI： 10.11959/j.issn.1000-436x.2022082.

Yong ZHANG, Dandan LI, Lu HAN, et al. Privacy-protected crowd-sensed data trading algorithm[J]. Journal on communications, 2022, 43(5): 1-13. DOI： 10.11959/j.issn.1000-436x.2022082.

摘要

为解决群体感知数据交易模式下参与者数据隐私泄露的问题，提出了一种隐私保护的群体感知数据交易算法。首先，为实现对参与者的隐私保护，设计了基于差分隐私的聚合方案，参与者不再需要上传原始数据，而是按照任务需求对收集的数据进行分析和计算，将任务结果按照平台分配的隐私预算添加噪声后发送给平台；其次，为确保参与者的可信性，构建了参与者的信誉模型；最后，为激励消费者和参与者参与交易，在考虑消费者对结果偏差的容忍约束和参与者的隐私泄露补偿的基础上构建了交易优化模型以优化平台的收益，并给出了基于遗传算法的收益优化算法（POA）来求解该模型。仿真结果表明，POA不仅保护了参与者的隐私，而且在平台的收益方面相比于VENUS和DPDT分别提高了29.27%和20.45%。

Abstract

To solve the problem that data privacy leakage of participants under the crowd-sensed data trading model

a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly

to achieve the privacy protection of participants

an aggregation scheme based on differential privacy was designed.Participants were no longer needed to upload raw data

but analyzed and calculated the collected data according to the task requirements

and then sent the analysis results to the platform after adding noise in accordance with the privacy budget allocated by the platform to protect their privacy.Secondly

in order to ensure the credibility of participants

a reputation model of participants was proposed.Finally

in order to encourage consumers and participants to participate in transactions

a data trading optimization model was constructed by considering the consumer’s constraint on the result deviation，the participant’s privacy leakage compensation and platform profit

and a POA based on genetic algorithm was proposed to solve the model.The simulation results show that the POA not only protects the privacy of participants

but also increases the profit of the platform by 29.27% and 20.45% compared to VENUS and DPDT

respectively.

关键词

Keywords

references

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