AI-powered face recognition has become essential to various IoT applications, including home automation, security systems, and personalized services. While these systems offer significant advancements, they still face critical challenges related to accuracy and privacy. One major issue is class imbalance, which is common in face recognition systems where certain demographic groups are underrepresented. This imbalance results in biased models, compromising the accuracy and fairness of these systems. Furthermore, traditional centralized training methods can expose sensitive facial data, raising serious privacy concerns. Federated Learning (FL) has emerged as a solution to improve model training by enabling collaboration across devices without sharing sensitive data. However, it also worsens the issue of data heterogeneity. This paper proposes a Hierarchical Federated Learning (HFL) framework to address class imbalance while preserving privacy. By aggregating local models at different hierarchical levels, the framework mitigates data imbalance and enhances fairness in face recognition systems. Additionally, a privacy-preserving mechanism based on Secure Multi-Party Computation (SMPC) is implemented to ensure data security during the training process.

We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains, with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach. The prevailing consensus mechanisms, initially crafted for cryptocurrency applications, prove unsuitable for the unique dynamics of supply chain systems. Unlike the broad inclusivity of cryptocurrency networks, our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria. The delineation of roles for supply chain participants within the consensus process becomes paramount. While reputation serves as a well-established quality parameter in various domains, its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory. Moreover, recognizing the primary role of efficient block verification in blockchain-enabled supply chains, our work introduces a comprehensive reputation model. This model strategically selects a leader node to orchestrate the entire block mining process within the consensus. Additionally, we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model. Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism, contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.

Machine learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service quality, along with fast implementation and integration with the edge and cloud services. However, the execution of ML in an edge-cloud environment introduces privacy risks to data owners (e.g., patients). In this article, we present a privacy-preserving ML framework by leveraging microservice technology for safeguarding healthcare IIoT systems. More specifically, we develop a microservice-based distributed privacy-preserving technique using differential privacy (DP) and a radial basis function network (RBFN) to balance between privacy protection and model performance in edge networks. We conduct extensive experiments to evaluate the performance of the proposed technique. The results revealed that DP has a significant influence on the model’s performance and achieves more than 90% accuracy with an epsilon value over 0.4, enhancing data protection and analytics through the implementation of microservices.

We propose a privacy-preserving ensemble infused enhanced deep neural network (DNN)-based learning framework in this article for Internet of Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, the edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local data set suffers from low accuracy, which can be improved by the aforementioned convergence and ensemble learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, ensemble learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a differential privacy-based privacy-preserving DNN with transfer learning for a local model generation to ensure minimal loss and higher efficiency at the edge server. We conduct several experiments to evaluate the performance of our proposed framework.

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL mitigates the privacy issues of the traditional collaborative learning model by aggregating multiple locally trained models without sharing any datasets among the participants. Nevertheless, the FL-based DL (FDL) model cannot be trusted as it is susceptible to intermediate results and data structure leakage during the model aggregation process. The proposed framework introduces an edge and cloud-powered service-oriented architecture identifying the key components and a service model for residual networks-based FDL with differential privacy for generating trustworthy locally trained models. The service model decomposes the functionality of the overall FDL process as services to ensure trustworthy execution through privacy preservation. Finally, we develop a privacy-preserving local model aggregation mechanism for FDL. We perform several experiments to assess the performance of the proposed framework.

This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users’ queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework.

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

We present a reversible biosignal steganography method to authenticate the source of biosignal in this paper. Cloud is being a popular platform for storing a large volume of biosignals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmogram (PPG). However, outsourcing biosignals to the cloud may introduce authenticity issues. For instance, patient data can be altered, or fake patient data can be inserted by the dishonest cloud service provider or attacker for giving benefits to business organizations such as insurance service providers. Steganography approaches can be used to hide data source's identification data before outsourcing to the cloud for maintaining authenticity. Existing biosignal steganography approaches fail to reconstruct original biosignal after applying a reverse data hiding technique. In other words, current biosignal steganography approaches are irreversible. Reversible biosignal steganography method is required for protecting biosignal data from deterioration and efficient use by its stakeholders. In this work, we develop a reversible biosignal steganography approach using the Extended Binary Golay Code based error correction method. Our proposed method embeds secret authentication message as an error within different types of biosignals such as ECG, PPG, and EEG. Extended Binary Golay Code based error correction method is used to extract the secret message, and reconstruct original biosignal. We conduct a set of experiments for evaluating the performance of our proposed method.