Local Differential Privacy for Physical Sensor Data and Sparse Recovery
This addresses privacy concerns in sensor data for applications like environmental monitoring, but it is incremental as it focuses on a specific 1D case and algorithm.
The paper tackled the problem of recovering heat source locations from locally differentially private thermal sensor data, achieving recovery of the general geographic vicinity while maintaining privacy, with performance analyzed via Earth Mover Distance error.
In this work we explore the utility of locally differentially private thermal sensor data. We design a locally differentially private recovery algorithm for the 1-dimensional, discrete heat source location problem and analyse its performance in terms of the Earth Mover Distance error. 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. We also discuss the relationship between the property of an inverse problem being ill-conditioned and the amount of noise needed to maintain privacy.