SecLoc: Encryption system based on compressive sensing measurements for location estimation

In this paper we present an efficient encryption system based on Compressive Sensing, without the additional computational cost of a separate encryption protocol, when applied to indoor location estimation problems. The breakthrough of the method is the use of the weakly encrypted measurement matrices which are generated when solving the optimization problem to localize the source. It must be noted that in this method an alternative key is required to secure the system.

[1]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[2]  Philippe Jacquet,et al.  WLAN-based indoor path tracking using compressive RSS measurements , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[3]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.

[4]  George Tzagkarakis,et al.  Bayesian compressed sensing imaging using a Gaussian Scale Mixture , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  George Tzagkarakis,et al.  Multiple-measurement Bayesian compressed sensing using GSM priors for DOA estimation , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Maria Papadopouli,et al.  Low-dimensional signal-strength fingerprint-based positioning in wireless LANs , 2014, Ad Hoc Networks.