Local Differential Privacy for Physical Sensor Data and Sparse Recovery

被引:0
作者
Gilbert, Anna C. [1 ]
McMillan, Audra [1 ]
机构
[1] Univ Michigan, Dept Math, Ann Arbor, MI 48109 USA
来源
2018 52ND ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS (CISS) | 2018年
关键词
sparse signal recovery; graph diffusion; differential privacy; graph tomography;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this work, we exploit the ill-posedness of linear inverse problems to design algorithms to release differentially private data or measurements of the physical system. We discuss the spectral requirements on a matrix such that only a small amount of noise is needed to achieve privacy and contrast this with the ill-conditionedness. We then instantiate our framework with several diffusion operators and explore recovery via l(1) constrained minimisation. Our work indicates that it is possible to produce locally private sensor measurements that both keep the exact locations of the heat sources private and permit recovery of the "general geographic vicinity" of the sources.
引用
收藏
页数:6
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