基于自适应连续时间的群智感知轨迹隐私保护方案
Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing
- 电子学报 2023年51卷第10期 页码：2894-2901
- 作者机构：
1.湖南工商大学前沿交叉学院,湖南长沙 410205
2.武汉理工大学计算机与人工智能学院,湖北武汉 430070
3.湘江实验室,湖南长沙 410205
4.湖南工商大学计算机学院,湖南长沙 410205
- 作者简介：
[ "蒋伟进 男，1964年7月出生于湖南省益阳市.现为湖南工商大学计算机学院二级教授、博士、博士生导师.主要研究方向为联邦学习、群智感知、边缘计算、社会计算. E-mail: jwjnudt@163.com" ]
[ "王海娟（通讯作者） 女，2000年8月出生于江西省九江市.现为湖南工商大学前沿交叉学院硕士研究生.主要研究方向为隐私安全、群智感知. E-mail: 2502560411@qq.com" ]
[ "周为 男，2000年5月出生于湖南省益阳市.现为湖南工商大学计算机学院硕士研究生.主要研究方向为隐私安全、群智感知. E-mail: 1216330671@qq.com" ]
[ "陈艺琳 女，2000年9月出生于河南省许昌市.现为湖南工商大学计算机学院硕士研究生.主要研究方向为联邦学习、信息安全. E-mail: 1986746095@qq.com" ]
[ "吴玉庭 女，1998年4月出生于湖南省益阳市.现为湖南工商大学计算机学院硕士研究生.主要研究方向为联邦学习、群智感知. E-mail: 1321224262@qq.com" ]
[ "韩裕清 男，2000年11月出生于湖南省长沙市.现为湖南工商大学计算机学院硕士研究生.主要研究方向为联邦学习、边缘计算、群智感知. E-mail: 897735614@qq.com" ]
- 基金信息：国家自然科学基金(61772196;72088101)
DOI：10.12263/DZXB.20230359
中图分类号： TP309收稿：2023-04-21，
修回：2023-08-25，
纸质出版：2023-10-25
- 稿件说明：
移动端阅览
蒋伟进,王海娟,周为等.基于自适应连续时间的群智感知轨迹隐私保护方案[J].电子学报,2023,51(10):2894-2901.
JIANG Wei-jin,WANG Hai-juan,ZHOU Wei,et al.Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing[J].ACTA ELECTRONICA SINICA,2023,51(10):2894-2901.
蒋伟进,王海娟,周为等.基于自适应连续时间的群智感知轨迹隐私保护方案[J].电子学报,2023,51(10):2894-2901. DOI： 10.12263/DZXB.20230359.
JIANG Wei-jin,WANG Hai-juan,ZHOU Wei,et al.Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing[J].ACTA ELECTRONICA SINICA,2023,51(10):2894-2901. DOI： 10.12263/DZXB.20230359.
摘要
针对轨迹差分隐私保护存在的预测精度差、隐私预算分配效用低的问题，本文提出自适应连续时间序列下的群智感知轨迹预测方案.首先在任务分配阶段，为参与者分配轨迹路线；其次引入隐马尔可夫模型（Hidden Markov Model，HMM），对轨迹进行预测；然后使用预分配和自适应分配相结合的综合隐私预算分配方法，降低隐私预算；最后利用拉普拉斯机制，进行位置扰动.实验结果表明，与相关工作相比，所提方法兼顾预测性和低预算性，对群智感知中参与者在轨迹隐私安全保护上具有良好的保护效果.
Abstract
In response to the problems of poor prediction accuracy and low utility of privacy budget allocation in trajectory differential privacy protection
our paper proposes an adaptive trajectory prediction scheme for continuous time series in crowdsensing. Firstly
in the task assignment phase
trajectory routes are assigned to participants. Then
the HMM (Hidden Markov Model) is introduced to predict the trajectories. Next
a comprehensive privacy budget allocation method combining pre-allocation and adaptive allocation is used to reduce the privacy budget. Finally
the laplace mechanism is applied to perturb the locations. Experimental results show that compared with related work
the proposed method achieves a balance between prediction accuracy and low budget requirements
and provides good privacy protection for participants in trajectory privacy security in crowdsensing.
关键词
Keywords
references
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