Dynamic Network Data Protection Algorithm Using Differential Privacy in Internet of Things

Federated learning (FL), which enables multiple distributed devices (clients) to collaboratively train a global model without transmitting their private data, has attracted much attention in the Internet of Things (IoT) domain. Compared with centralized learning, FL has obvious privacy advantages because it can protect the clients’ raw data from direct access by adversaries. Furthermore, to prevent the adversaries from inferring private information from the transmitted parameters, several FL algorithms based on differential privacy (DP) have been proposed, where the clients add artificial noise to their local parameters for privacy protection. Nevertheless, the added noise would disrupt the learning process and degrade the performance of the trained model. Considering this, in this article, we develop a performance-enhanced DP-based FL (PEDPFL) algorithm, where a classifier-perturbation regularization method is proposed to improve the robustness of the trained model against DP-injected noise. We derive the theoretical privacy and convergence analysis of the proposed algorithm, and also demonstrate the influence of some hyperparameters on the convergence performance. Simulation results on real-world data sets show that the proposed algorithm has better classification performance than the existing DP-based FL algorithms at the same level of privacy protection, and thus, it is more applicable to IoT applications.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

Internet of Things (IoT) links uniquely identifiable objects (things) to their virtual representations in the internet. Meanwhile, wireless sensor networks are widely used in IoT and various sensors which collect different information containing the identity, status, location of a object or any other business, social or privately relevant information. But some issues related to sensor's location privacy will be paid attention to. In this paper, we focus on protecting the sensor's location by introducing suitable modifications to sensor routing to make it difficult for an eavesdropper to find the original location. And we propose a flexible routing strategy, called Multi-routing Random Walk, which protects the sensor's location. Our strategy can efficiently reduce the chance of packets being detected.

It is our great pleasure to welcome you to the 2015 ACM Workshop on IoT Privacy, Trust, and Security -- IoTPTS'15. At a basic level, the Internet of Things (IoT) refers simply to networked devices, but the IoT vision consists of a complex ecosystem that ranges from cloud backend services and big-data analytics to home, public, industrial, and wearable sensor devices and appliances. Architectures for these systems are in the formative stages, and IoTPTS 2015 gives researchers and practitioners a unique opportunity ensure privacy, trust, and security are designed into these systems from the beginning. This is the inaugural year of the IoTPTS Workshop and the call for papers attracted 13 submissions worldwide from 12 countries and 4 continents. The final program contained 5 papers (representing an acceptance rate of 38%) and a keynote IoT Security and Privacy: Threats and Challenges by Dr. Yong Ho Hwang from the Software R&D Center at Samsung Electronics.

Chapter 3 - Integrated machine learning techniques for preserving privacy in Internet of Things (IoT) systems

The Internet of Things (IOT) Refers to the billions and millions of physical devices around the world that are now connected to the Internet, a possible of intelligence attached to many central features of the modern world such as cities, hospitals, networks, organizations, government transactions, and buildings. Privacy and Security are key issues preventing widespread adoption of the Internet of Things. In this paper, with example scenarios, we provide a review of Privacy and security attacks from the perspective of the layers that make up the Internet of Things. In addition, we will take an extended example of one type of iot which is the smart home IOT system and give a review of the methods that provide solutions to these problems along with their limitations. To overcome these limitations, and some of the security challenges regarding the iot domain and we are going to discuss some of the security issues that are still open, and we have made recommendations for future work with a framework. Further research and implementation of the framework and our recommendations will enhance the robustness and reliability of the Internet of Things and its applications against a variety of known attacks.

This paper presents HC-LBR routing, the first location-based routing (LBR) mechanism that simultaneously retains high communication efficiency and privacy for Internet of Things (IoT). Existing LBR schemes were originally designed for wireless sensor networks (WSNs). Although they offer attractive efficiency and scalability, their privacy performance presents a vulnerability when being used in IoT. This is because, unlike a conventional WSN where all nodes are owned by the same user, an IoT network has an open architecture in which nodes owned by different users are mixed and work together. Therefore, when LBR is directly used in an IoT network, the sharing of location information among alien nodes allows one user to peek into the communication privacy of other users. HC-LBR overcomes this privacy weakness by computing efficient geographic routes directly based on Hilbert-Curve-encrypted location information. HC-LBR consists of two components: A Kademlia-tree-based routing algorithm that supports efficient geographic routing in HC encrypted space, and Rand-Mix, a lightweight traffic mixer that uses multiple traffic flows to enhance the privacy of HC routing. Our simulation-based evaluation verifies the high efficiency and privacy of the proposed methods.

The Internet of Things (IoT) has witnessed rapid proliferation, revolutionizing the way we interact with technology in various domains of life. This ubiquitous network of interconnected devices, however, presents serious challenges concerning privacy and security. As IoT devices gather vast amounts of sensitive data and share it across networks, ensuring the protection of user privacy and guarding against potential security breaches becomes paramount. This abstract delves into the primary concerns surrounding IoT privacy and security, addressing issues such as data collection, user consent, data ownership, and vulnerability to cyber-attacks. Furthermore, it explores potential solutions, including encryption, blockchain technology, and the implementation of robust authentication mechanisms to safeguard IoT ecosystems. Acknowledging the significance of effective regulation and industry collaboration, this abstract concludes by emphasizing the need for continued research and proactive measures to address the evolving landscape of IoT privacy and security challenges.

Wireless Sensor Network (WSN) technology has developed substantially over the past decade or so and now numerous solutions exist across a diverse range of innovative applications. The expanding Internet of Things (IoT) sector is becoming an ever more important aspect of modern technology and a key motivator for improving security and privacy in WSNs. Typically, WSN protocols form an integral part of the overall IoT infrastructure by enabling the sensor to access point communication links. These wireless links inherently encompass security challenges, frequently due to external interference and intrusions. As IoT applications incorporate WSNs in their architecture, the incentive to attack and compromise these WSNs escalates. Often, commercial off the shelf devices and standardized open-access protocols combine to achieve specific WSN deployments. Numerous WSN vulnerabilities exist, whilst attack approaches are abundant and change frequently. Thus, to ensure acceptable performance, safety and privacy in many IoT applications, the adopted WSN must be secure. This paper discusses IoT security and privacy, by evaluating a machine learning approach for interference detection focused entirely on analyzing received In-phase (I) and Quadrature-phase (Q) samples. Significantly, once an intrusion is detected, mitigation strategies can be implemented, thus emphasizing the requirement for interference detection. Random Forest is chosen as the machine learning classifier as it consists of a large number of individual decision trees operating as an ensemble. An intrusion detection system (IDS) is developed based on Matlab simulated ZigBee data as an initial insight into whether a real wireless data approach may be viable.

As a new type of distributed machine learning technology, federated learning has shown great application potential in the Internet of things, health care, smart home, finance and other fields. Its core advantage is that it can conduct model training without centralized data, effectively reducing the cost of data transmission and storage, and avoiding the risk of privacy disclosure. However, with the wide application of Federated learning, the problems of data security and privacy protection are increasingly apparent, especially in the face of complex network attacks and data leakage risks. This paper deeply analyzes the basic principles and architecture of Federated learning, and discusses the possible privacy threats in data transmission, model updating and participating devices in detail. Combined with the existing security protection technologies, such as differential privacy, encryption algorithm and secure multi-party computing, this paper discusses how to effectively ensure the security of Federated learning. Finally, the article also looks forward to the future development trend of Federated learning in privacy protection, model optimization, computational efficiency and cross domain collaboration, aiming to provide theoretical support and practical guidance for the further development and application of this technology.

Block-HRG: Block-based differentially private IoT networks release

In the era heavily influenced by Internet of Things (IoT), prioritizing strong security and the protection of user privacy is of utmost importance. This comprehensive review paper embarks on a meticulous examination of the multifaceted challenges and risks facing IoT security and privacy. It encompasses the hardware, software, and data‐in‐transit domains, shedding light on potential vulnerabilities and associated threats. In response to these concerns, this paper puts forth recommendations for effective strategies to mitigate these risks. Providing a road‐map for enhancing security and privacy in IoT environments. Furthermore, this review thoroughly assesses a multitude of solutions proposed by various authors, with the primary aim of enhancing security and privacy within the IoT landscape. The analysis provides insights into the strengths and limitations of these solutions. This is aiding in the development of a holistic comprehension of the existing status of IoT security and privacy. Moreover, the paper delves into the complexities surrounding integrating emerging technologies into the IoT framework. It explores the obstacles and challenges inherent in this process and proposes potential strategies to address these hurdles. By doing so, the review provides a holistic perspective on existing security and privacy enhancement technologies and offers guidance on navigating the dynamic landscape of emerging technologies within the IoT domain. Publications included in the review consist of journal articles, conference papers, and book chapters from reputable sources indexed in SCI (Science Citation Index), Scopus, and Web of Science.

Internet of Things (IoT): From awareness to continued use

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The integration of Internet of Things (IoT) technologies within smart healthcare systems has revolutionized medical diagnostics, patient monitoring, and treatment personalization, the widespread deployment of interconnected medical devices introduces a complex landscape of security and privacy risks, exposing sensitive health data to cyber threats and unauthorized access. As healthcare systems generate vast volumes of real-time data across distributed environments, traditional security mechanisms fall short in ensuring end-to-end protection, especially under the resource constraints of IoT devices. Artificial intelligence (AI) has emerged as a transformative force, enabling proactive threat detection, intelligent risk scoring, and adaptive mitigation strategies that operate in dynamic healthcare ecosystems. This chapter presents a comprehensive analysis of IoT security architectures in smart healthcare, examining vulnerabilities at each system layer and highlighting AI-driven frameworks for intrusion detection, anomaly recognition, and privacy-preserving data analytics. Advanced methods such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation are explored as essential tools for safeguarding patient information while maintaining model utility and interoperability. The convergence of AI and cybersecurity offers a resilient approach to managing evolving threats without compromising clinical efficiency or regulatory compliance. Key challenges, including scalability, explainability, and cross-border data governance, are critically assessed, along with future directions for building robust, ethical, and intelligent healthcare infrastructures.