The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.

Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT

Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.

Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.

Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

With the continuous development of computer technology and the advent of the information age, the amount of information in the network continues to grow. Information overload seriously affects people’s efficiency in using information. In order to solve the problem of information overload, various personalized recommendation technologies are widely used. This paper proposes a collaborative filtering recommendation algorithm based on differential privacy protection, which provides privacy protection for users’ personal privacy data while providing effective recommendation service.

Improved collaborative filtering recommendation algorithm based on differential privacy protection

Short text is a popular text form, which is widely used in short commentary, micro-blog and many other fields. With the development of the social software and movie websites, the size of data is also becoming larger and larger. Most data is useless for us while other data is important for us. Therefore, it is very necessary for us to extract the useful short text from the big data. However, there are some problems such as fewer features, irregularity on the short text classification. To solve the problem we should pretreat the short text set and choose the significant features.  This paper use semi-supervised learning and SVM to improve the traditional method and it can classify a large number of short texts to mining the useful massage from the short text.  The experimental results also show a good improvement.

A New SVM Method for Short Text Classification Based on Semi-Supervised Learning

Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users’ location-based behaviors are divided into different time windows. For each window, the extractor achieves users’ timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.

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Chunyong Yin

Papers

Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

Improved collaborative filtering recommendation algorithm based on differential privacy protection

A New SVM Method for Short Text Classification Based on Semi-Supervised Learning

Mobile marketing recommendation method based on user location feedback