Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy

Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance

Using Federated Artificial Intelligence System of Intrusion Detection for IoT Healthcare System Based on Blockchain

Recently Internet of things (IoT)-based healthcare system has expanded significantly, however, they are restricted by the absence of an intrusion detection mechanism (IDS). Modern technologies like blockchain (BC), edge computing (EC), and machine learning (ML) provide a robust security solution that is well-suited to protecting patients' medical information. In this study, we offer an intelligent intrusion detection mechanism FIDANN that protects the confidentiality of medical data by completing the intrusion detection task by utilising Dwarf mongoose-optimized artificial neural networks (DMO-ANN) through a federated learning (FL) technique. In the context of recent developments in blockchain technology, such as the elimination of contaminating attacks and the provision of complete visibility and data integrity over the decentralized system with minimal additional effort. Using the model at the edges secures the cloud from attacks by limiting information from its gateway with less computing time and processing power as FL works with fewer datasets. The findings demonstrate that our suggested models perform better when dealing with the diversity of data produced by IoT devices

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events

Protection of data privacy based on artificial intelligence in Cyber-Physical Systems

With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules