Low-complexity Multiclass Encryption by Compressed Sensing

TL;DR

This paper introduces a low-complexity, multiclass encryption scheme using compressed sensing, enabling different recovery qualities for various receiver classes while maintaining resource efficiency and some security benefits.

Contribution

It proposes a novel multiclass encryption method based on compressed sensing with theoretical security analysis and practical applications demonstrating degraded signal recovery.

Findings

Lower-class receivers have significantly reduced recovery quality.

The scheme offers provable bounds on recovery performance differences.

Compressed sensing provides some security at minimal additional cost.

Abstract

The idea that compressed sensing may be used to encrypt information from unauthorised receivers has already been envisioned, but never explored in depth since its security may seem compromised by the linearity of its encoding process. In this paper we apply this simple encoding to define a general private-key encryption scheme in which a transmitter distributes the same encoded measurements to receivers of different classes, which are provided partially corrupted encoding matrices and are thus allowed to decode the acquired signal at provably different levels of recovery quality. The security properties of this scheme are thoroughly analysed: firstly, the properties of our multiclass encryption are theoretically investigated by deriving performance bounds on the recovery quality attained by lower-class receivers with respect to high-class ones. Then we perform a statistical analysis of the measurements to show that, although not perfectly secure, compressed sensing grants some level of security that comes at almost-zero cost and thus may benefit resource-limited applications. In addition to this we report some exemplary applications of multiclass encryption by compressed sensing of speech signals, electrocardiographic tracks and images, in which quality degradation is quantified as the impossibility of some feature extraction algorithms to obtain sensitive information from suitably degraded signal recoveries.