  In recent years, a large scale of various wireless sensor networks have been
deployed for basic scientific works. Massive data loss is so common that there
is a great demand for data recovery. While data recovery methods fulfil the
requirement of accuracy, the potential privacy leakage caused by them concerns
us a lot. Thus the major challenge of sensory data recovery is the issue of
effective privacy preservation. Existing algorithms can either accomplish
accurate data recovery or solve privacy issue, yet no single design is able to
address these two problems simultaneously. Therefore in this paper, we propose
a novel approach Privacy-Preserving Compressive Sensing with Multi-Attribute
Assistance (PPCS-MAA). It applies PPCS scheme to sensory data recovery, which
can effectively encrypts sensory data without decreasing accuracy, because it
maintains the homomorphic obfuscation property for compressive sensing. In
addition, multiple environmental attributes from sensory datasets usually have
strong correlation so that we design a MultiAttribute Assistance (MAA)
component to leverage this feature for better recovery accuracy. Combining PPCS
with MAA, the novel recovery scheme can provide reliable privacy with high
accuracy. Firstly, based on two real datasets, IntelLab and GreenOrbs, we
reveal the inherited low-rank features as the ground truth and find such
multi-attribute correlation. Secondly, we develop a PPCS-MAA algorithm to
preserve privacy and optimize the recovery accuracy. Thirdly, the results of
real data-driven simulations show that the algorithm outperforms the existing
solutions.
