A survey of machine and deep learning methods for privacy protection in the Internet of things

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.This work is partially supported by the Generalitat de Catalunya under grant 2017 SGR 962 and the HORIZON-GPHOENIX (101070586) and HORIZON-EUVITAMIN-V (101093062) projects.Peer ReviewedPostprint (published version

Automatic non-biting midge (Chironomidae) identification through the application of object detection and deep learning techniques

This research study introduces a possible new method for the identification of chironomid larvae mounted on microscope slides in the form of an automatic computer-based identification tool using deep learning techniques. Deep learning is becoming an important tool for ecologists where there are advantages and limitations for its use as a rapid biomonitoring tool. Chironomids collected from the River Stour in Kent had their head capsules mounted on microscope slides and images of these were then captured using a Raspberry PI. Using these images, a series of object detection models were created to classify several different chironomid genera. These models were then used to show how different deep learning approaches, focusing on pre-training preparation, could improve the performance of image classification. The model comparisons included two object detection frameworks (Faster-RCNN and SDD frameworks), three balanced image sets (with and without augmentation) and variations of two hyperparameter values (Learning Rate and Intersection Over Union). All models were reported using the standard computer science object detection evaluation protocol, the mean average precision metric. Each model configuration was run three times,to allow for statistical significance evaluation. Additionally, a series of novel post training performance metrics were created examining a model’s prediction accuracy and its givenconfidence value in its prediction choice. The highest mean average precision value achieved was 0.751 by Faster-RCNN. The models highlighted significance between the two object detection frameworks, where the Faster-RCNN framework performed better than SDD framework; however, there was non-significance between the image sets and the hyperparameters values. All models produced similar accuracy results regardless of framework used (between 95.5%-97.7%), however, there were large differences between the confidence examinations, wherein Faster-RCNN produced more confident predictions than SSD. In conclusion, this investigation successfully developed object detection models using SSD and Faster-RCNN to classify between three chironomid genera. As a proof of concept, this study highlighted that automatic and rapid classification models using deep learning techniques can be applied for the correct taxonomic identification of difficult organisms, like chironomid larvae, further advancing the prospect of using this relatively new field of computer science for ecological research

Dictionary of privacy, data protection and information security

The Dictionary of Privacy, Data Protection and Information Security explains the complex technical terms, legal concepts, privacy management techniques, conceptual matters and vocabulary that inform public debate about privacy. The revolutionary and pervasive influence of digital technology affects numerous disciplines and sectors of society, and concerns about its potential threats to privacy are growing. With over a thousand terms meticulously set out, described and cross-referenced, this Dictionary enables productive discussion by covering the full range of fields accessibly and comprehensively. In the ever-evolving debate surrounding privacy, this Dictionary takes a longer view, transcending the details of today''s problems, technology, and the law to examine the wider principles that underlie privacy discourse. Interdisciplinary in scope, this Dictionary is invaluable to students, scholars and researchers in law, technology and computing, cybersecurity, sociology, public policy and administration, and regulation. It is also a vital reference for diverse practitioners including data scientists, lawyers, policymakers and regulators