Title: Local Differential Privacy for Physical Sensor Data and Sparse Recovery,
Abstract: 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.