Robust and Efficient Privacy Preservation in Industrial IoT via correlation completion and tracking The Industrial IoT (IIoT) is a key element of Industry 4.0, bringing together modern sensor technology, fog  cloud computing platforms, and artificial intelligence (AI) to create smart, selfoptimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information continuously increases, giving rise to serious privacy concerns. In this work we address the problem of efficiently and effectively tracking the structure of multivariate streams recorded in a network of IIoT devices. The time varying correlation data values are used to add noise which maximally preserves privacy, in the sense that it is very hard to be removed. To improve communication efficiency between connected IoT devices, we exploit low rank properties of the correlation matrices, and track the essential correlations from a small subset of correlation values estimated by a subset of network nodes. Extensive simulation studies, validate the correctness, efficiency, and effectiveness of our approach in terms of computational complexity, transmission energy efficiency and privacy preservation.

Bidirectional Information Flow and the Roles of Privacy Masks in CloudBased Control We consider a cloudbased control architecture for a linear plant with Gaussian process noise, where the state of the plant contains a clientxe2x80x99s sensitive information. We assume that the cloud tries to estimate the state while executing a designated control algorithm. The mutual information between the clientxe2x80x99s actual state and the cloudxe2x80x99s estimate is adopted as a measure of privacy loss. We discuss the necessity of uplink and downlink privacy masks. After observing that privacy is not necessarily a monotone function of the noise levels of privacy masks, we discuss the joint design procedure for uplink and downlink privacy masks. Finally, the tradeoff between privacy and control performance is explored.