Partitioning based incremental marginalization algorithm for anonymizing missing data streams The IoT and its applications are the inseparable part of modern world. IoT is expanding into every corner of the world where internet is available. IoT data streams are utilized by many organizations for research and business. To benefit from these data streams, the data handling party must secure the individualsxe2x80x99 privacy. The most common privacy preservation approach is data anonymization. However, IoT data provides missing data streams due to the varying device pool and preferences of individuals and unpredicted devicesxe2x80x99 malfunctions of IoT. Minimization of missingess and information loss is very important for anonymizing of missing data streams. To achieve this, we introduce IncrementalPBM (Incremental Partitioning Based Marginalization) for anonymizing missig data streams. IncrementalPBM utilizes time based sliding window for missing data stream anonymization, and it aims to control the number of QIDs for anonymization while increasing the number of tuples for anonymization. Our experiment on real dataset showed IncrementalPBM is effective and efficient for anonymizing missing data streams compared to existing missing data stream anonymization algorithm. IncrementalPBM showed significant improvement; 5% to 9% less information loss, 4500 to 6000 more number of reuse anonymization while showing comparable clustering, suppression and runtime.

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.