Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A deep learning technique is widely used to analyze the potential value of these data due to its unprecedented performance in both the academic and industrial communities. However, the data generated from the IoT devices are distributed among different users. Directly combining these data to a central server will cause privacy leakage, especially for personal sensitive data. Rather than centralized training by getting access to all these raw data, an alternative is to collaboratively learn a model in a distributed manner. However, there exist two main challenges in a distributed learning setting. The first one is how to preserve the privacy of users. The second one is to reduce the communication burden (e.g., mobile users have limited bandwidth) due to high-frequent data exchange. To address these two challenges, we design a communication efficient and privacy-preserving framework to enable different participants to distributively learn a model with a privacy protection guarantee. In particular, we develop a differentially private approximate mechanism for the distributed deep learning. In addition, we design a new gradient sparsification method to, at the first time, reduce both upload and download communication costs. The performance of the proposed framework is tested under different neural network structures for different data sets including, image classification and mobile sensor data. The experimental results demonstrate that we can reduce the communication up to only 2% compared to the full gradients exchange and achieve up to 16% accuracy increase compared to the previous works.

Integrating Internet of Things (IoTs) devices with secure smart home networks assisted by the cloud signifies a cutting-edge and potent tool for contemporary home automation. This allows various appliances and devices in a home remotely controlled by the internet to communicate and share data. The typical smart home system depends on the cloud service or centralized server, which makes them further vulnerable to potential security breaches and single points of failure. As a decentralized nature, Blockchain (BC) distributes the control and storage of data across the network, preventing unauthorized attacks. Integrating BC technology into the protected smart home network boosts the system's dependability, safety, and privacy. In addition, machine learning (ML) and analytics offer behaviour analysis and predictive maintenance for optimized energy consumption. Finally, combining IoT with cloud-assisted security transforms homes into smart, connected ecosystems, offering convenience without integrating confidentiality or dependability. Accordingly, this study presents a BC-based Deep Learning in the Secure Smart Home Network (BPDL-SSHN) methodology in the IoT-cloud platform. In the BPDL-SSHN methodology, BC technology permits secret proficient data from the smart home network. Furthermore, the BPDL-SSHN method follows a series of processes to detect malicious activities such as Binary Fox Optimization Algorithm (BFOA) based feature selection, Attention-based Long Short-Term Memory (ALSTM)-based classification, and Harbor Seal Whiskers Optimization (HSWO)-based hyperparameter tuning. The HSWO method's design helps better the hyperparameter choice of the ALSTM method, significantly enhancing the recognition performance. The comparative outcome of the BPDL-SSHN methodology reported the proficient solution of the smart home network to detect and monitor malicious or harmful activities. The experimental outcome implied that the BPDL-SSHN methodology accomplishes a maximum accuracy performance of 98.91% over other approaches.

The Internet of Things (IoT) has revolutionized data collection and analysis from diverse sources. IoT-based monitoring systems are now widespread in manufacturing, healthcare, and smart cities. These systems gather vast amounts of data from sensors and devices, enabling the detection of anomalies and patterns. The IoT has become an integral part of our lives, transforming various industries by enabling seamless connectivity between devices and increasing automation and efficiency. However, the reliability of IoT systems is often compromised due to the complexity and scale of these networks, making them vulnerable to failures and security breaches. To mitigate this problem, anomaly detection using efficient learning in IoT is a promising solution to identify unusual patterns that could indicate system faults or threats. This paper implements an efficient learning-driven approach for anomaly detection classification in IoT-based monitoring systems. The study aims to improve the accuracy and speed of identifying unusual patterns or behaviours in IoT data streams. By using advanced machine learning techniques, the proposed method can effectively detect anomalies in real-time, enhancing the overall security and reliability of IoT monitoring systems. The proposed framework enhances IoT system reliability by reducing downtime, improving security, and ensuring consistent performance of connected devices. Experimental results demonstrate the efficiency of machine learning techniques and their capabilities in anomaly detection. Empirical findings show that Decision Trees (DT) and Random Forests (RF) outperform other competitive models like Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Adaptive Boosting (AB) and Voting Classifier (VC) for network intrusion detection in the context of anomaly detection.

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and discusses the challenges of applying them in the context of IoT. Moreover, we present a privacy-preserving inference approach that runs a lightweight neural network at IoT objects to obfuscate the data before transmission and a deep neural network in the cloud to classify the obfuscated data. Evaluation based on the MNIST dataset shows satisfactory performance.

Most current Internet of Things systems face many problems like privacy leakage, information island and low application value, because they are designed from the perspective of Internet of Things service providers. However, the practicality and privacy security of Internet of Things services are more important from the perspective of Internet of Things system users. Considering that there aren’t too many Internet of Things devices to use and manage for ordinary people and service, a more service-oriented architecture of the lightweight private IOT remote system is proposed in this paper. To verify this system, verifications like remote sensors, remote actuators, and a micromixing application are developed and illustrated. Owing to its distributed server architecture, this system has high flexibility, practicability, parallelism, and low coupling. This novel system architecture provides a new perspective to develop practical valuable private Internet of Things services.

The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in the computer science major, where the Raspberry Pi platform is used as an effective vehicle to greatly enhance students’ learning performance and experience. The devised course begins with learning simple hardware and moves to building a whole prototype system. This paper illustrates the outcome of the proposed approach by demonstrating the prototype IoT systems designed and developed by students at the end of one such IoT course. Furthermore, this study provides insights and lessons regarding how to facilitate the use of the Raspberry Pi platform to successfully achieve the goals of project-based teaching and learning in IoT.

The Internet of Things (IoT) is becoming a buzz word due to the extensive application of it. It can be found anywhere nowadays, Healthcare, Smart cities, Smart energy, smart buildings and so on. The internet of thing has been a subject of improvement in terms of the price, size and persistence, but one of the main challenges in understanding IoT is in dealing with the device and data heterogeneities and establishing the interoperability between devices, information, and services built on top of it. Therefore, Ontologies can bring the change we are aiming in establishing a standard inter-devices communication and enable interoperability in m2m communication that allows data and data semantics to be described in application-independent ways.

Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness. HDC models have already been proven to be useful in different learning applications, especially in resource-limited settings such as the increasingly popular Internet of Things (IoT). One class of learning tasks that is missing from the current body of work on HDC is graph classification. Graphs are among the most important forms of information representation, yet, to this day, HDC algorithms have not been applied to the graph learning problem in a general sense. Moreover, graph learning in IoT and sensor networks, with limited compute capabilities, introduce challenges to the overall design methodology. In this paper, we present GraphHD - a baseline approach for graph classification with HDC. We evaluate GraphHD on real-world graph classification problems. Our results show that when compared to the state-of-the-art Graph Neural Networks (GNNs) the proposed model achieves comparable accuracy, while training and inference times are on average <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$14.6\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.0 \times$</tex> faster, respectively.

Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical oper... | Find, read and cite all the research you need on Tech Science Press

To address the asynchronous challenges stemming from user resource constraints and intermittent network connections in distributed Internet of Things (IoT) systems, asynchronous federated learning (AFL) has been extensively explored in both academic and industrial domains. However, existing AFL approaches still struggle to effectively tackle the issue of model aggregation information loss due to delayed users, which leads to degraded model accuracy. To alleviate the impact of delayed or unavailable model updates on model aggregation, we propose a novel model update enhancement method to compensate for the loss of model aggregation information. Specifically, we utilize delayed model updates as an update agent for this user and correct the weights of these updates within the delayed rounds threshold to ensure the delayed user’s contribution to the model aggregation. Additionally, we integrate symmetric homomorphic encryption (SHE) into AFL to ensure user privacy while simultaneously minimizing computational overhead. Lastly, we conduct extensive experiments to demonstrate that our scheme improves model accuracy by 6.53% compared to state-of-the-art solutions. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MiaoGroup-XDU/acc-improved-ppafl</uri>.

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster.

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.

In the process of the continuous reform of modern social economic system and the continuous development of science and technology, the distribution of the Internet of things reflects good application value. However, the Internet of things has many distributed nodes, data center, high computing cost and poor supervision. In the process of privacy protection, there are still some objective problems. Therefore, the use of blockchain technology in the privacy protection of the Internet of things privacy data block, structure decentralization, information hierarchy and other aspects, which can establish a more perfect Internet of things privacy protection model and more rich management mechanism, making the prevention of privacy leakage, data tampering and other aspects of privacy protection work has been further developed. Therefore, this paper takes the implementation mode of blockchain smart contract in smart home privacy protection as an application example to analyze the application value of blockchain in the privacy protection of Internet of things.

Recently, most of the Internet of things (IoT) infrastructures are highly centralized with single points of failure, which results in serious security and privacy issues of IoT data. Fortunately, blockchain technique can provide a decentralized and secure IoT framework to deal with security issues based on the characteristics of decentralization, non-tampering, openness, transparency, and traceability. However, the blockchain consensus protocol guarantees the safety and reliability of data, but it also brings problems such as scalability limitations and poor storage extensibility, resulting in the inability to directly integrate blockchain and the IoT in existing conditions. In this article, a private three-tier local blockchain, Three-tier architecture Blockchain (TBchain), is proposed to solve the problem by splitting part of the transactions in the public blockchain and locking them in a higher-level blockchain TBchain. Additionally, the private blockchain TBchain is connected to the public blockchain to build a hierarchical blockchain network to provide privacy protection for the IoT data stored on the blockchain. Finally, we implement an IoT framework based on TBchain and the InterPlanetary File System (IPFS) to realize the decentralized IoT, which guarantees the user’s access control right to personal data. Experimental results show that the IoT framework based on TBchain and IPFS realizes the user’s access control right to personal data by verifying in advance to ensure the confidentiality and security of shared data, and improves the security and privacy of IoT data and transactions. Moreover, we prove that the scalability and storage extensibility of the blockchain is positively correlated with the number of data blocks in TBchain.