The microbiome plays an important role in a wide variety of skin disorders Not only is the skin microbiome altered, but also surprisingly many skin diseases are accompanied by an altered gut microbiome The microbiome is a key regulator for the immune system, as it aims to maintain homeostasis by communicating with tissues and organs in a bidirectional manner Hence, dysbiosis in the skin and/or gut microbiome is associated with an altered immune response, promoting the development of skin diseases, such as atopic dermatitis, psoriasis, acne vulgaris, dandruff, and even skin cancer Here, we focus on the associations between the microbiome, diet, metabolites, and immune responses in skin pathologies This review describes an exhaustive list of common skin conditions with associated dysbiosis in the skin microbiome as well as the current body of evidence on gut microbiome dysbiosis, dietary links, and their interplay with skin conditions An enhanced understanding of the local skin and gut microbiome including the underlying mechanisms is necessary to shed light on the microbial involvement in human skin diseases and to develop new therapeutic approaches

https://biblio.ugent.be/publication/8701391/file/8701392.pdf

Gut-Skin Axis: Current Knowledge of the Interrelationship between Microbial Dysbiosis and Skin Conditions.

In recent years, the Internet of Things (IoT) has made great progress. The interconnection between IoT and the Internet enables real-time information processing and transaction implementation through heterogeneous intelligent devices. But the security, the privacy, and the reliability of IoT are key challenges that limit its development. The features of the blockchain, such as decentralization, consensus mechanism, data encryption, and smart contracts, are suitable for building distributed IoT systems to prevent potential attacks and to reduce transaction costs. As a decentralized and transparent database platform, blockchain has the potential to raise the performance of IoT security to a higher level. This article systematically analyzes state of the art of IoT security based on the blockchain, paying special attention to the security features, issues, technologies, approaches, and related scenarios in blockchain-embedded IoT. The integration and interoperation of blockchain and IoT is an important and foreseeable development in the computational communication system.

Embedding Blockchain Technology Into IoT for Security: A Survey

Edge and fog computing for IoT: A survey on current research activities & future directions

Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the emerging Internet of Things (IoT) has gained a lot of attention from the government, academia, and industries in recent years. Federated cybersecurity (FC) is regarded as a revolutionary concept to make the IoT safer and more efficient in the future. This emerging concept has the potential of detecting security threats, taking countermeasures, and limiting the spreading of threats over the IoT network system efficiently. An objective of cybersecurity is achieved by forming the federation of the learned and shared model on top of various participants. Federated learning (FL), which is regarded as a privacy-aware ML model, is particularly useful to secure the vulnerable IoT environment. In this article, we start with background and comparison of centralized learning, distributed on-site learning, and FL, which is then followed by a survey of the application of FL to cybersecurity for IoT. This survey primarily focuses on the security aspect but it also discusses several approaches that address the performance issues (e.g., accuracy, latency, resource constraint, and others) associated with FL, which may impact the security and overall performance of the IoT. To anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts, challenges, and research trends in this area. With this article, readers can have a more thorough understanding of FL for cybersecurity as well as cybersecurity for FL, different security attacks, and countermeasures.

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

— Literature review articles are essential to summarize the related work in the selected field. However, covering all related studies takes too much time and effort. This study questions how Artificial Intelligence can be used in this process. We used ChatGPT to create a literature review article to show the stage of the OpenAI ChatGPT artificial intelligence application. As the subject, the applications of Digital Twin in the health field were chosen. Abstracts of the last three years (2020, 2021 and 2022) papers were obtained from the keyword "Digital twin in healthcare" search results on Google Scholar and paraphrased by ChatGPT. Later on, we asked ChatGPT questions. The results are promising; however, the paraphrased parts had significant matches when checked with the Ithenticate tool. This article is the first attempt to show the compilation and expression of knowledge will be accelerated with the help of artificial intelligence. We are still at the beginning of such advances. The future academic publishing process will require less human effort, which in turn will allow academics to focus on their studies. In future studies, we will monitor citations to this study to evaluate the academic validity of the content produced by the ChatGPT.

OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare

The advance of Internet-of-Things (IoT) techniques has promoted an increasing number of organizations to explore more mission-critical solutions. However, the response latency, bandwidth usage, and reliability are still challenging issues in the traditional IoT. To tackle these challenges, the fog-based IoT has become popular and the range query is one of the most frequently used operations in fog-based IoT, where given a range query, a fog node will return the aggregated data from IoT devices to the query user. Because the fog nodes are not fully trusted, there is a desire to design a privacy-preserving range query scheme in the fog-based IoT. However, most of existing privacy-preserving range query schemes are not efficient in terms of communication overhead, especially for a large-size range. Therefore, it is still a challenging issue to design a communication-efficient range query in fog-based IoT. Aiming at this challenge, in this article, we propose a new privacy-preserving range query scheme in the fog-based IoT. Specifically, we first devise an efficient homomorphic encryption scheme for maintaining data privacy and security in a range query. Then, we present a novel range decomposition technique to compile the range query, which can transform a given range query $[L,U]$ , where $0\leq L \leq U \leq n-1$ , into a semi-triangular structure, and enable our proposed scheme to achieve $O(\log ^{3}n)$ communication efficiency. The detailed security analysis shows that our proposed scheme is really privacy preserving, and the extensive performance evaluation demonstrates that our proposed scheme is efficient in terms of low communication overhead and the computational cost.

Achieving O(log³n) Communication-Efficient Privacy-Preserving Range Query in Fog-Based IoT

The boom of Internet of Things devices promotes huge volumes of eHealthcare data will be collected and aggregated at eHealthcare provider. With the help of these health data, eHealthcare provider can offer reliable data service (e.g., k-NN query) to doctors for better diagnosis. However, the IT facility in the eHealthcare provider is incompetent with the huge volumes of eHealthcare data, so one popular solution is to deploy a powerful cloud and appoint the cloud to execute the k-NN query service. In this case, since the eHealthcare data are very sensitive yet cloud servers are not fully trusted, directly executing the k-NN query service in the cloud inevitably incurs privacy challenges. Apart from the privacy issues, efficiency issues also need to be taken into consideration because achieving privacy requirement will incur additional computational cost. However, existing focuses on k-NN query do not (fully) consider the data privacy or are inefficient. For instance, the best computational complexity of k-NN query over encrypted eHealthcare data in the cloud is as large as $O(k\log ^{3} N)$
 , where N is the total number of data. In this paper, aiming at addressing the privacy and efficiency challenges, we design an efficient and privacy-preserving k-NN query scheme for encrypted outsourced eHealthcare data. Our proposed scheme is characterized by integrating the k d-tree with the homomorphic encryption technique for efficient storing encrypted data in the cloud and processing privacy-preserving k-NN query over encrypted data. Compared with existing works, our proposed scheme is more efficient in terms of privacy-preserving k-NN query. Specifically, our proposed scheme can achieve k-NN computation over encrypted data with $O(lk\log N)$
 computational complexity, where l and N respectively denote the data dimension and the total number of data. In addition, detailed security analysis shows that our proposed scheme is really privacy-preserving under our security model and performance evaluation also indicates that our proposed scheme is indeed efficient in terms of computational cost.

Achieving Efficient and Privacy-Preserving k -NN Query for Outsourced eHealthcare Data

A privacy-preserving and non-interactive federated learning scheme for regression training with gradient descent

The sixth-generation (6G) communication technology has been attracting great interests from both industry and academia, as it is regarded as a promising approach to achieve more stable and low-latency communication. These promising features of 6G make it an enabler for cybertwin, a technique to create digital representations for physical objects to implement various functionalities. In this article, we consider a cybertwin-based spatiotemporal keyword query service over a dynamic message data set in intelligent transportation system (ITS) scenarios. Particularly, in the considered service, publishers upload messages to the cloud, and each cybertwin predictively launches queries to retrieve messages on behalf of the corresponding vehicle, such that each vehicle can timely receive messages that are of its interest whenever it arrives at a location. Nevertheless, as the cloud is not fully trustable, there exist privacy concerns related to the messages and queries. Up to now, although many schemes have been proposed to handle privacy-preserving spatial, temporal, or keyword queries, none of them can simultaneously support queries containing both spatial, temporal, and keyword criteria on dynamic data sets. Aiming at the issue, we design a layered index based on segment trees to dynamically organize messages containing both spatial, temporal, and keyword information. Moreover, based on a symmetric homomorphic encryption scheme, we encrypt the messages and queries and present a two-server privacy-preserving spatiotemporal keyword query scheme. We analyze the security of the proposed scheme and also conduct extensive experiments to evaluate its performance. The results show that our proposed scheme is indeed privacy preserving and computationally efficient.

Toward Privacy-Preserving Cybertwin-Based Spatiotemporal Keyword Query for ITS in 6G Era

Similarity query over time series data plays a significant role in various applications. Meanwhile, driven by the reliable and flexible cloud services, encrypted time series data are often outsourced to the cloud, so the similarity query over encrypted time series data has recently attracted considerable attention. Nevertheless, existing solutions still have issues in supporting similarity queries over time series data with different lengths, query accuracy and query efficiency. To address these issues, in this paper, we propose an efficient and privacy-preserving similarity range query scheme, where the time warp edit distance (TWED) is used as the similarity metric. Specifically, we first organize time series data into a kd-tree by leveraging TWED's triangle inequality and design an efficient similarity range query algorithm for the kd-tree. Second, based on a symmetric homomorphic encryption technique, we devise a suite of privacy-preserving protocols to provide a security guarantee for kd-tree based similarity range queries. After that, by using the similarity range query algorithm and these protocols, we propose our similarity query scheme, in which we elaborate two strategies to make our scheme resist the cloud inference attack. Finally, the security analysis and performance evaluation demonstrate that our scheme is indeed privacy-preserving and efficient.

Yandong Zheng

Papers

Achieving O(log³n) Communication-Efficient Privacy-Preserving Range Query in Fog-Based IoT

Achieving Efficient and Privacy-Preserving k -NN Query for Outsourced eHealthcare Data

A privacy-preserving and non-interactive federated learning scheme for regression training with gradient descent

Toward Privacy-Preserving Cybertwin-Based Spatiotemporal Keyword Query for ITS in 6G Era

Efficient and Privacy-Preserving Similarity Range Query over Encrypted Time Series Data