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%。
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 partic-ipants,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 re-sults 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.Fi-nally,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 com-pensation 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.
作者
张勇
李丹丹
韩璐
黄小红
ZHANG Yong;LI Dandan;HAN Lu;HUANG Xiaohong(School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《通信学报》
EI
CSCD
北大核心
2022年第5期1-13,共13页
Journal on Communications
基金
国家重点研发计划基金资助项目(No.2020YFE0200500)
北京邮电大学优秀博士生创新基金资助项目(No.CX2019212)。
关键词
群体感知
数据交易
差分隐私
信誉模型
crowd sensing
data trading
differential privacy
reputation model
