Secure and scalable smart grid IoT communication through quantum key distribution, homomorphic encryption, and federated learning

Recent advancements in reactor designs could offer new revolutionary capabilities, including remote monitoring, increased flexibility, and reduced operation and maintenance costs. Embracing new digital technologies would allow for operational concepts such as semiautonomous or near-autonomous control, and two-way communications for real-time integration with the upcoming smart electric grid. However, such continuous data transmission from and toward a reactor site could potentially introduce new challenges and vulnerabilities, necessitating the prioritization of cybersecurity. Conventional information technology–based encryption schemes, which rely mostly on computational complexity, have been shown to be vulnerable to cyberattacks. With the advent of quantum computing, practically any asymmetric encryption could be potentially compromised. For example, it has been shown that a RSA-2048 bit key could be broken in 8 h. To address this challenge, we explore the feasibility of quantum key distribution (QKD) to secure communications. QKD is a physical layer security scheme relying on the laws of quantum mechanics instead of mathematical complexity. QKD promises not only unconditional security but also detection of potential intrusion, as it allows the communication parties to become aware of eavesdropping. To test the proposed hypothesis, a novel simulation tool (NuQKD) was developed to allow for real-time simulation of the BB84 QKD protocol between two remote terminals. A reference scenario is proposed, generic enough to cover various internal and external communication links to a reactor site. Using NuQKD, the internal and external data links were modeled, and receiver operating characteristic curves were calculated for various QKD configurations. A performance analysis was conducted, demonstrating that QKD can provide adequate secret key rates with low false alarms and has the potential of addressing the nuclear industry’s high standards of confidentiality for distance lengths up to 75 km of fiberoptics. Using a conservative estimation, QKD can provide up to 21.5 kbps of secret key rate for a distance of 1 km and 14.4 kbps at 10 km. The target secret key rates for the corresponding links were estimated at 16 kbps and 80 bps, respectively, based on the analysis of real data from a PUR-1 fully digital reactor. Consequently, QKD is shown to be compatible with existing and future point-to-point reactor communication architectures. These results motivate further study of quantum communications for nuclear reactors.

As federated learning (FL) keeps dominating the way in privacy-preserving machine learning (ML), notably in finance, its long-term sustainability is confronted with the approaching challenge of quantum computing. The introduction of quantum computing poses a challenge to traditional cryptographic technique on which FL relies RSA, elliptic curve cryptography (ECC), and some homomorphic encryption (HE) methods applied for encrypting model updates during training rendering this chapter to explore the interplay between the threat of quantum computing and the FL architectures, with emphasis on the implications this interaction introduces to cybersecurity and privacy in finance applications. Quantum computers are theoretically capable of solving hard math problems that have been regarded as computationally intractable for quantum computers. The well-known algorithms like Shor’s and Grover’s provides serious risk to the cryptographic procedures FL pipelines depends on. The finance sector, by its nature involving sensitive data and dependence on secure, distributed ML systems, is particularly vulnerable. The chapter begins by summarizing the fundamentals of FL and current encryption methods adapted to protect FL workflows. It then shifts towards the essence of quantum computing, emphasizing on the notable algorithms that threaten FL. We also explore potential attack points through which quantum computing could attack FL, e.g., model inversion attacks, poisoning attacks facilitated by quantum capabilities, and threats to secure aggregation. Post-quantum cryptography (PQC) is also presented as a potential counterattack, followed by a discussion of its use case in FL platforms. This chapter attempts to close the gap between FL and quantum computing threats to the financial sector. It is an introduction to practitioners and researchers to revisit examining security assumptions in FL and move towards the quantum-augmented cyber threat model.

FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks

False Data Injection Attacks (FDIAs) pose a substantial risk to the reliability and stability of Cyber-Physical Power Systems (CPPS). While federated learning (FL) has emerged as a promising approach to detect such attacks without exposing sensitive data, security concerns remain in FL, including untrusted central aggregators and potential malicious client updates. This research integrate a private Ethereum blockchain layer and homomorphic encryption into a secure FL framework for FDIA detection to verify model updates and authenticate participating nodes. We design smart contracts to immutably log model update hashes and enforce client authentication, enhancing traceability and tamper-resistance. A prototype implementation uses Ethereum smart contracts for model update verification and client identity management. We simulate the blockchain-integrated FL on a cyber-physical power system dataset using three detection models – XGBoost, LSTM, and a Transformer – and analyze the blockchain-induced latency and communication overhead under a specific network configuration. Results show that the blockchain layer has negligible impact on detection accuracy (global AUC ∼ 0.94-0.96 across models) while introducing a moderate training time overhead (∼ 13–40% increase in training duration due to block confirmation delays). The proposed research demonstrates a viable approach to blockchain-aided federated learning for critical infrastructure security, combining data privacy, model integrity, and participant trust in a unified framework.

<sec>Quantum key distribution (QKD) is a pivotal technology in the field of secure communication by using the principles of quantum mechanics to implement theoretically unbreakable encryption. However, QKD faces significant challenges in achieving large-scale deployment. The primary hurdle lies in the scarcity of quantum resources, especially entangled photon pairs, which are fundamental to protocols such as Ekert91. In traditional QKD implementations, only a small potion of the generated entanglement pairs contribute to generating the original key, resulting in lower efficiency and resource waste. Resolving this limitation is crucial to the advancement and scalability of QKD networks.</sec><sec>This paper introduces an innovative approach to QKD by integrating the multiscale entanglement renormalization ansatz (MERA), a technique which is originally developed for many-body quantum systems. By utilizing MERA’s hierarchical structure, the proposed method not only improves the efficiency of entanglement distribution but also reduces the consumption of quantum resources. Specifically, MERA compresses many-body quantum states into lower-dimensional representations, allowing for the transmission and storage of entanglement in a more efficient manner. This compression significantly reduces the number of qubits required, optimizing both entanglement utilization and storage capacity in quantum networks.</sec><sec>To evaluate the performance of this method, we conduct simulations under standardized conditions. In the simulation, a 1024-bit encryption request, an 8% error rate, an average path length of 4 hops in the quantum network, and a 95% success rate for link entanglement generation and entanglement swapping operations are assumed. These parameters reflect the real physical conditions in contemporary QKD networks. The results demonstrate that compared with traditional QKD protocols, the MERA-based approach saves 124151 entangled pairs, which is impressive. This significant reduction in resource consumption indicates the potential application of MERA in improving the efficiency of QKD systems without sacrificing security. Importantly, the security of the key exchange process remains intact, for the method inherently adheres to the principles of quantum mechanics, particularly the no-cloning theorem and the use of randomness in the decompression layer.</sec><sec>Some conclusions can be drawn below. The MERA not only enhances the scalability of QKD by optimizing quantum resource allocation, but also maintains the necessary security guarantees for practical cryptographic applications. By integrating MERA into existing QKD frameworks, we can significantly reduce the resource overhead and make large-scale, secure quantum communication more feasible. These findings contribute a new dimension to the field of quantum cryptography, indicating that advanced quantum many-body techniques like MERA have the potential to unlock the full potential of quantum networks in real world.</sec>

Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data. FL promises the privacy of clients and its security can be strengthened by cryptographic methods such as additively homomorphic encryption (HE). However, the efficiency of FL could seriously suffer from the statistical heterogeneity in both the data distribution discrepancy among clients and the global distribution skewness. We mathematically demonstrate the cause of performance degradation in FL and examine the performance of FL over various datasets. To tackle the statistical heterogeneity problem, we propose a pluggable system-level client selection method named Dubhe, which allows clients to proactively participate in training, meanwhile preserving their privacy with the assistance of HE. Experimental results show that Dubhe is comparable with the optimal greedy method on the classification accuracy, with negligible encryption and communication overhead.

Abstract: This book explores the convergence of quantum communication technologies and decentralized blockchain frameworks, offering a comprehensive guide to building secure, scalable, and resilient quantum communication infrastructures. With quantum computing threatening classical cryptographic systems, there is an urgent need to develop quantum-resistant and quantum-native protocols. The book investigates post-quantum cryptographic primitives, quantum key distribution (QKD), and blockchain-integrated quantum consensus models. Special emphasis is given to the design and deployment of hybrid quantum-classical nodes, real-time authentication systems, and decentralized identity protocols. Through detailed architectural models and case studies in finance, healthcare, and national security, this work lays the groundwork for resilient, secure data exchange networks of the future. It aims to inform academics, technologists, policymakers, and quantum engineers navigating this technological frontier. Keywords: quantum blockchain, decentralized networks, quantum key distribution, post-quantum cryptography, secure communication, consensus algorithms, hybrid systems, protocol design, resilient infrastructure, quantum internet

It is the fact that wireless local area networks are increasingly deployed by businesses, government and SOHO users as they offer many advantages to its customers with mobility, flexibility, convenience etc. It opened a wide range of new commercial areas for hardware vendors, at low cost. This justifies why wireless networks have become one of the most widely used communication systems in the world. However, since there are no boundaries in wireless networks, they are vulnerable to security threats than wired networks. Therefore, providing secure communication for wireless networks has become one of the prime concerns. Quantum cryptography, to be precise, Quantum Key Distribution (QKD), offers the promise of unconditional security. In this paper, we extend our previous research work of how QKD can be used in IEEE 802.11 wireless networks to ensure secure key distribution. Our contributions in this paper are as follows: (1) We discussed how QKD can be used in IEEE 802.11 wireless networks to securely distribute the keys. (2) We use new protocol QKD. (3) We introduced a method that take the advantage of mutual authentication features offered by some EAP variants of 802.1X Port-Based Network Access Control. (4) Finally, we present a new code called Quantum Message Integrity Code (Q-MIC) which provides mutual authentication between the two communication parties. Also experimental results are presented with Simulink Model.

The advancement of healthcare informatization has resulted in the exponential growth of patient data stored across various hospitals, laboratories, and clinical centers, properly integration and analysis of this data and use it for machine learning can help make medical processes more efficient. However, privacy regulations and institutional silos pose substantial barriers to collaborative research and centralized model training. Here, Federated Learning (FL) has surfaced as an innovative distributed learning approach, as it empowers institutions to jointly build models while safeguarding raw data privacy. This review outlines FL’s fundamentals and highlights its applications across multiple healthcare domains, including medical image analysis, clinical outcome prediction, and wearable health monitoring. The fundamental FL designs (horizontal FL, vertical FL, and split FL learning) and privacy-enhancing methods (safe aggregation, homomorphic encryption, and differential privacy) are discussed. Additionally, we also examine recent advances in adaptive privacy mechanisms, asynchronous updates and explainable AI to support clinical integration. The study concludes with a discussion of current limitations and future research directions, such as multimodal FL, personalized modeling, and edge-based computing.

Secure and efficient multi-key aggregation for federated learning

Smart grids are electricity networks that can intelligently integrate the action of all connected users - producers, consumers, and prosumers (i.e., producers and consumers at the same time) - in order to efficiently deliver sustainable, economic and secure electricity supplies. Smart grids rely on a two-way communication infrastructures in which energy measurement data taken at users located smart meters, as well as control information, must be exchanged in real-time or - at least - near real-time, while simultaneously guaranteeing a secure and privacy preserving communication. This paper addresses such a problem by presenting a distributed communication architecture that guarantees the privacy of fine-grained users data while enabling the energy supplier to access aggregate energy measurements and per-user coarse-grained data for billing purposes. The key idea underlying the proposed architecture is to adopt a secure multiparty computation method based on a verifiable secret sharing of the keys used by the smart meters to encrypt their sensitive data. This approach allows to remove the need for any intermediate aggregator element with clear benefits in terms of scalability and robustness.

Quantum computing represents a transformative advancement in computational power, with profound implications for cryptographic systems. This paper explores the intersection of quantum computing and cryptography, examining the potential for quantum computers to both break traditional cryptographic algorithms and enable new forms of secure communication. Classical cryptographic systems, such as RSA and ECC, rely on the computational difficulty of problems like integer factorization and discrete logarithms. However, quantum algorithms, notably Shor's algorithm, can solve these problems exponentially faster than the best-known classical algorithms, posing a significant threat to the security of current cryptographic practices. This vulnerability necessitates the development and implementation of quantum-resistant algorithms to ensure data security in a post-quantum world. The paper reviews the state-of-the-art in quantum-resistant cryptographic algorithms, including lattice-based, hash-based, code-based, and multivariate polynomial cryptography. These algorithms are analyzed for their security properties, efficiency, and practicality for real-world deployment. Additionally, the paper discusses the ongoing standardization efforts led by organizations such as NIST, which are crucial for establishing widely accepted quantum-safe cryptographic protocols. Beyond the threat posed by quantum computing, this paper also highlights the potential benefits quantum computing can bring to cryptographic systems. Quantum Key Distribution (QKD) is one such advancement, offering theoretically unbreakable encryption by leveraging the principles of quantum mechanics. QKD enables secure communication channels that are immune to any computational advances, including those of quantum computers, by ensuring that any attempt at eavesdropping can be detected. The practical implementation challenges of QKD and other quantum cryptographic protocols are also addressed, including issues related to transmission distance, error rates, and integration with existing communication infrastructures. Experimental results from recent QKD trials are presented, showcasing significant progress in extending the viability of quantum-secure communication over long distances. Furthermore, the paper delves into the hybrid approaches combining classical and quantum cryptographic techniques. These approaches aim to leverage the strengths of both paradigms to enhance security and performance. For instance, hybrid protocols might use classical encryption for bulk data transfer, supplemented by quantum techniques for key exchange, thereby ensuring robust security even against future quantum adversaries. In conclusion, while quantum computing poses substantial risks to existing cryptographic systems, it also offers novel opportunities for creating highly secure communication protocols. The transition to quantum-safe cryptography is imperative to protect sensitive information in the quantum era. This paper underscores the urgency of advancing quantum-resistant cryptographic research, promoting international collaboration for standardization, and exploring innovative applications of quantum technologies in cryptography. Continued research and development in these areas are essential to stay ahead of the evolving threat landscape and harness the full potential of quantum computing for secure communications.

Federated Learning (FL) has emerged as a suitable option for collaborative, user-centric machine learning while protecting sensitive user data. FL differs significantly from traditional centralized frameworks that collect raw data on a central server. FL focuses on collecting model updates generated locally on edge devices. With traditional FL approaches, data must be downloaded to a central server for model training. In contrast, training occurs on the edge and only model updates are sent. This decentralized framework, unlike traditional FL approaches, is ideal for ubiquitous applications—systems woven seamlessly into everyday surroundings like smart homes, advanced mobile devices, and IoT systems—where sensitive data is constantly and automatically generated. In these systems, user privacy is paramount, and advanced methods must be implemented to ensure privacy and system efficacy. This paper discusses the integration of FL into ubiquitous environments, addressing the relevance, advantages, and disadvantages of this approach. I’d like to focus on privacy-preserving methods, but their preservation against data leakage and adversary attacks adds significant value. There are case studies to show the challenges encountered, lessons learned, and the use of FL within privacy-centric ubiquitous systems. In closing this paper, I summarize the key points and underscore the transformational potential of FL for privacy-sensitive, innovative systems. I also promote further research in this area to support its practical relevance. This paper demonstrates that among the many approaches to privacy within pervasive computing, FL is the only one that is practical and scalable.

Federated learning (FL) refers to a system of training and stabilizing local machine learning models at the global level by aggregating the learning gradients of the models. It reduces the concern of sharing the private data of participating entities for statistical analysis to be carried out at the server. It allows participating entities called clients or users to infer useful information from their raw data. As a consequence, the need to share their confidential information with any other entity or the central entity called server is eliminated. FL can be clearly interpreted as a privacy-preserving version of traditional machine learning and deep learning algorithms. However, despite this being an efficient distributed training scheme, the client’s sensitive information can still be exposed to various security threats from the shared parameters. Since data has always been a major priority for any user or organization, this article is primarily concerned with discussing the significant problems and issues relevant to the preservation of data privacy and the viability and feasibility of several proposed solutions in the FL context. In this work, we conduct a detailed study on FL, the categorization of FL, the challenges of FL, and various attacks that can be executed to disclose the users’ sensitive data used during learning. In this survey, we review and compare different privacy solutions for FL to prevent data leakage and discuss secret sharing (SS)-based security solutions for FL proposed by various researchers in concise form. We also briefly discuss quantum federated learning (QFL) and privacy-preservation techniques in QFL. In addition to these, a comparison and contrast of several survey works on FL is included in this work. We highlight the major applications based on FL. We discuss certain future directions pertaining to the open issues in the field of FL and finally conclude our work.

The latest growth of storage capabilities has led to an accumulating volume of medical data stored locally by various healthcare entities. Given the recent progress observed in the domain of artificial intelligence, these data could be efficiently exploited, leading to improved and less expensive healthcare conditions. However, the common practice is medical data to be solely locally used, ending up poorly exploited, due to strict sharing restrictions stemming mainly from privacy limitation of their sensitive nature. Considering the centralized character of conventional machine learning approaches, it is apparent that they cannot reassure the privacy required. On the other hand, federated learning (FL) can be regarded as an upcoming and promising answer to efficient exploitation of medical data, considering its decentralized approach. In more details, FL can enable collaboration among various participating entities on the development and training of a common, central and fully shared model without need of sharing owned sensitive data. Thus, apparently FL approach not only can mitigate the privacy-preservation issues but can lead to the development of reliable and robust healthcare tools. Indicatively, FL can facilitate the development of a model capable of assessing the health risk, which can be a vital tool for medical sciences. To this end, in this work we present a tool capable of assessing the occurrence of different diseases or complications. This tool is based on FL technique utilizing deep neural network model. The FL model developed herein is indicatively applied to four different medical applications proving its generality in the healthcare domain. The FL approach discussed herein is compared with a corresponding centralized learning. According to the demonstrated results, FL can consist a useful health risk assessment tool exhibiting acceptable performance while preserving privacy in sensitive medical data.