Ziyang Ji, Jie Zhang, Yuji Dong, Ka Lok Man · 6 authors
Effective management of private keys is crucial to ensure the security and ownership of users’ data and digital assets in the Web3 environment. However, existing solutions often fail to adequately address private key management from the user’s perspective. Private key leakage and loss incidents occur frequently, resulting in significant losses of digital assets. Moreover, the conventional approach of revoking both the private and public keys after a leakage or loss accident is inconvenient in Web3, where the public key serves as the user’s wallet address or digital identity. To tackle the issue of user-side private key management in Web3, this paper presents KeyShield which is a leakage-and-loss-resilient private key protection scheme. KeyShield divides the user’s private key into three shares, securely stored across a primary device and a secondary device owned by the user, and a third storage module owned by the user or a semi-trusted service provider. For daily use of the private key, the user only needs to connect the primary and secondary devices. In the event of a leakage or loss, such as device theft or attack, an update process will be triggered to update the three shares, immediately invalidating the leaked or lost share while causing no changes to the public key. As a demonstration of KeyShield, we developed KeyShieldECC accessible on both Android and iOS platforms for managing Elliptic Curve Cryptography (ECC) private keys. The testing results show that for a 256-bit ECC private key, the daily use only needs 0.05 seconds and update needs 0.25 to 0.3 seconds on an ordinary smart phone.
The advent of next-generation networks, epitomized by Sixth-Generation (6G) wireless systems, signifies a paradigm shift from the simplistic goal of connectivity to a complex ecosystem defined by the convergence of the physical, digital, and biological worlds. This transition, characterized by hyper-density, extreme heterogeneity, and the integration of novel paradigms like terahertz (THz) communications, reconfigurable intelligent surfaces (RIS), and non-terrestrial networks (NTN), fundamentally invalidates many of the security assumptions of previous generations. The very characteristics that enable unprecedented data rates, ultra-low latency, and massive machine-type communications—such as massive Multiple-Input Multiple-Output (MIMO), distributed ledger technologies, and artificial intelligence (AI)-driven network slicing—also expand the attack surface, introducing novel vulnerabilities ranging from intelligent jamming and eavesdropping in the physical layer to sophisticated adversarial attacks on AI-based network management functions. This article provides a comprehensive exploration of secure transmission techniques designed for this nascent landscape. It moves beyond the traditional paradigm of cryptography-as-an-overlay to advocate for a holistic, interdisciplinary approach where security is embedded as a foundational property across all protocol layers. The discussion commences with a critical re-evaluation of the evolving threat landscape, identifying key vulnerabilities unique to next-generation architectures. Subsequently, it delves into advanced physical layer security (PLS) techniques, demonstrating how the intrinsic randomness of the wireless channel can be leveraged for secret key generation and covert communications, particularly in the context of massive MIMO and THz bands. The narrative then transitions to the cryptographic layer, examining the imperative shift towards post-quantum cryptography (PQC) to counter the looming threat of quantum decryption, alongside the role of blockchain and distributed ledgers in establishing decentralized trust in a network devoid of fixed infrastructure. A significant portion of the article is dedicated to AI-native security, exploring both the potential of AI to create autonomous, self-healing security mechanisms and the critical vulnerabilities introduced by adversarial machine learning. The analysis culminates in an examination of securing the network’s foundational pillars, including the integrity of network slicing, the resilience of the Radio Access Network (RAN), and the security of non-terrestrial components. This article concludes that the security of next-generation networks is not merely a technical challenge but a foundational requirement for the socio-economic viability of a hyper-connected future, necessitating a continuous, adaptive, and unified security architecture that evolves in lockstep with the network itself.
Junhee LEE, Yixi Cai Lili lei Lei Li, Gweonho Jeong, Jihye Kim · 6 authors
Forward-secure digital signatures protect the integrity of past signatures, even if the current signing key is compromised. Among forward-secure signature schemes, the method introduced by Lee et al. [1], based on zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs), is particularly notable for achieving constant complexity across all metrics without requiring a predefined maximum time period. However, a naive approach to recursive proof composition results in an excessive amount of redundant computation being repeated for each signing process, which our method reduces significantly. In this paper, we advance a zk-SNARK-based forward-secure signature scheme by significantly improving the efficiency of its signing algorithm. By incorporating commit-and-prove SNARKs, we replace the inner verification process with commit verification within the signing circuit. Furthermore, we employ efficient recursive zk-SNARKs with accumulation and folding schemes to improve the setup and update algorithms. Our implementation demonstrates the practicality of our approach: the signing procedure completes in 0.18 seconds, achieving a 75-fold speedup over the previous scheme, setup time is reduced to 0.71 seconds - over 61 times faster, and public parameters are reduced to 25 MB, more than 16 times smaller.
C. Selvan, M. A. Gunavathie, Sini Anna Alex, Shaik Jaffar Hussain
ABSTRACT Appropriate routing strategies are necessary for mobile ad hoc networks (MANETs) in order to facilitate effective data transfer. In order to counter the prevailing problems, the correct routing schemes will need to be selected as the default configurations are used. In this paper, a special optimal link state routing (OLSR) protocol is proposed to incorporate a deep learning methodology to facilitate efficient video streaming in MANETs. This study presents a new improved variant of the OLSR protocol, which is specially tailored to achieve efficient video streaming in MANETs. It is a radical approach that combines a deep‐learning model with blockchain technology to overcome security and reliability issues. It starts with the gathering of video content that is available publicly. In order to detect black‐hole nodes, a special twin‐attention‐based Elman spiking neural network model is applied. The reliability of the neighboring nodes is then measured by means of trust values. The pufferfish optimization algorithm, or the accuracy‐aware energy‐efficient multipath routing algorithm (AEMRAP), which takes into account node‐ and link‐stability degrees, is used in making routing decisions. Interplanetary file system (IPFS) technology is used to store the data on blockchain and increase its security. The authentication of the blockchain architecture is conducted via the delegated proof‐of‐stake (DPoS) method that also delivers an extra protection of MANETs against unauthorized access. The study demonstrates superior performance in securing and optimizing video transmission, confirming that the extended OLSR protocol is highly effective for MANET video streaming applications. The proposed model exceeds the current approaches with a throughput of 2100 Kbps, an average end latency of 20.2 s, and a packet‐delivery ratio of 92.3%.
Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious key–value store encoding (OKVS) mechanism is introduced to enable secure and efficient key–value mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
Dileep Kumar Murala, Shadab Ahmad, V. A. Sankar Ponnapalli, Veera Ankalu Vuyyuru · 5 authors
Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren't enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren't always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module's permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain's decentralised trust and ML's adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.
Chi Zhang, Fenhua Bai, Xiaohui Zhang, Jinhua Wan · 6 authors
As a middleware technology in distributed computer systems, blockchain systems represent a paradigm for achieving node interconnectivity. Despite this, technical differences between various blockchain networks have led to the emergence of a phenomenon known as multi-chain, where inter-chain communication has become a trust barrier. Cross-chain technology is a powerful tool that allows data to flow between different blockchain networks, breaking down data barriers and enabling seamless data transfer. However, cross-chain identification may lead to potential risks such as the exposure of private information and data loss or tampering. In this brief, we propose Universal Cross-Chain Permissioned Blockchain (UCCPB) architecture, which connects single permissioned chains into a multi-chain system. Based on this, the Cross-Chain Anonymous Identity Authentication (CCAIA) model is proposed, which implements privacy-preserving chain identity registration and verification through zero-knowledge proof without a trusted setup. Furthermore, we propose the Proof of Cross-Chain Invocation (PoCI) mechanism of UCCPB, which consists of a node election and consensus on the invocation result. This mechanism ensures the correctness of the cross-chain invocation results and incentivizes nodes to participate in UCCPB. Our experiments show that the proposed UCCPB achieves a balance between performance and privacy while improving the security of cross-chain invocations.
Yu Gao, Carlo Campajola, Nicolò Vallarano, Andreia Sofia Teixeira · 5 authors
IOTA is a distributed ledger technology that relies on a peer-to-peer (P2P) network for communications. Recently an auto-peering algorithm was proposed to build connections among IOTA peers according to their “Mana" endowment, which is an IOTA internal reputation system. This paper’s goal is to detect potential vulnerabilities and evaluate the resilience of the P2P network generated using IOTA auto-peering algorithm against eclipse attacks. In order to do so, we interpret IOTA’s auto-peering algorithm as a random network formation model and employ different network metrics to identify cost-efficient partitions of the network. As a result, we present a potential strategy that an attacker can use to eclipse a significant part of the network, providing estimates of costs and potential damage caused by the attack. On the side, we provide an analysis of the properties of IOTA auto-peering network ensemble, as an interesting class of homophile random networks in between 1D lattices and regular Poisson graphs.
The paper explores the possibility of expanding the use of end-to-end encryption protocols based on the Double Ratchet algorithm in applications with low trust in the server, particularly in turn-based games and strategic interactions. The relevance of the research is due to the growing need for secure communication in cyberattacks, especially during military operations. The field of end-to-end encryption requires the study of additional applications beyond the usual ones, such as encrypted communication in text messengers. The developed implementation of the protocol can be safely used in any applications that aim to implement end-to-end encryption and satisfy the criterion of session ephemerality (in cases where secrets are stored outside a secure environment). The implemented server supports ephemeral sessions, which guarantee minimal risks of information compromise, and uses digital signatures (EdDSA) for user authentication. Logical routing of requests ensures efficient message transmission in secure scenarios. The choice of the classic game of checkers as an example allowed the authors to effectively demonstrate the advantages of end-to-end encryption and the capabilities of the implemented protocol. All cryptographic operations, including key generation, encryption and decryption of messages, are successfully performed on client devices. It is important to improve error handling mechanisms and optimize the operation of WebAssembly. An interesting area of further research is the creation of zero-knowledge proof mechanisms to prevent Man-In-The-Middle attacks during the creation of a shared secret, optimizing integration with cryptographic hardware security modules (HSM), and exploring the scalability of the solution. The proposed approach can be used to solve real-world information security problems where trust in the data transmission channel is critically important. Thus, the work has created a comprehensive solution that includes a cryptographic protocol, a backend, and a web client, which demonstrates the viability of end-to-end encryption in browser environments and multiplayer games. The work can be used as a basis for further research and development in the field of security of communication systems and privacy in multiplayer games.
Wencheng Chen, Jun Wang, Jeng‐Shyang Pan, R. Simon Sherratt · 5 authors
With the rapid expansion of sensor networks across domains such as environmental monitoring, industrial automation, and smart healthcare, ensuring secure and reliable data storage in resource-constrained environments has become a critical challenge. Traditional centralized storage systems struggle with data tampering, privacy leakage, and vulnerability to collusion among nodes. Blockchain technology, characterized by decentralization, immutability, and traceability, provides a promising foundation for trustworthy sensor data management. Among various consensus mechanisms, Delegated Proof of Stake (DPoS) has been recognized for its efficiency and low energy consumption, yet it faces two critical issues: limited incentives for ordinary sensor nodes to participate in voting and the risk of collusion that undermines fairness and stability. To overcome these limitations, this study proposes a blockchain-enabled sensor data storage framework incorporating a four-party evolutionary game model. The model explicitly captures the strategic interactions among cluster head nodes, ordinary sensor nodes, competing gateway nodes, and supervisory nodes, while integrating reputation evaluation, penalty enforcement, and supervisory oversight. Through evolutionary game analysis, the proposed framework reveals the stability conditions of node behaviors and identifies strategies that promote fair and secure consensus. Simulation results verify that the mechanism enhances node participation, suppresses collusion, accelerates consensus convergence, and achieves superior throughput and fault tolerance compared with existing schemes. This research provides theoretical insights and practical guidance for designing secure, efficient, and scalable blockchain-enabled sensor network data storage systems.
The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To address these challenges, this paper proposes Q-EnergyDEX, a zero-trust distributed energy trading framework driven by quantum key distribution and blockchain. The framework integrates physical-layer quantum randomness with market-level operations, providing an end-to-end quantum-secured infrastructure. A cloud-based Quantum Key Management Service continuously generates verifiable entropy and regulates key generation through a rate-adaptive algorithm to sustain high-quality randomness. A symmetric authentication protocol (Q-SAH) establishes secure and low-latency sessions, while the quantum-aided consensus mechanism (PoR-Lite) achieves probabilistic ledger finality within a few seconds. Furthermore, a Stackelberg-constrained bilateral auction couples market clearing with entropy availability, ensuring both economic efficiency and cryptographic security. Simulation results show that Q-EnergyDEX maintains robust key stability and near-optimal social welfare, demonstrating its feasibility for large-scale decentralized energy markets.
Russell W. F. Lai, Monisha Swarnakar, Ivy K. Y. Woo
The Learning with Errors (LWE) problem asks to distinguish noisy samples s^T A + e^T mod q from uniformly random values given the random matrix A. In this work, we show that a variant called Leaky LWE, where the distinguisher receives additionally noisy leakages (s^T, e^T) L + f^T of the LWE secret s and error e for low-norm matrix L chosen adaptively by the distinguisher after seeing A, is not easier than the standard LWE of the same dimensions up to polynomial losses in the noise level and the modulus. More generally, we show that the Leaky LWE problem is hard even if the public matrix A is structured and/or hinted and if the non-leaky parts of the secret and error do not follow Gaussian distributions, as long as the corresponding LWE problem without leakage is hard. Our reduction from LWE to Leaky LWE unifies and extends prior results on the Error-Leakage LWE problem [Döttling-Kolonelos-Lai-Lin-Malavolta-Rahimi, EUROCRYPT'23], where L only acts on the error e and the Hint-MLWE problem [Kim-Lee-Seo-Song, CRYPTO'23], where L is restricted to concatenations of random Gaussian scalar matrices not controlled by the distinguisher. Previously, the Hint-MLWE and Error-Leakage LWE assumptions were used as computational replacements of the statistical noise flooding technique in security proofs which led to improved parameters in lattice-based cryptographic constructions such as zero-knowledge proofs, threshold signatures and registration-based encryption. We provide lemmas which abstract out such computational arguments based on Leaky LWE.
Yenlik Begimbayeva, Temirlan Zhaxalykov, Amir Akhtanov, Ruslan Pashkevich · 6 authors
This research focuses on enhancing the security of decentralized quantum key distribution (QKD) networks, where the absence of a central authority creates significant challenges such as malicious node infiltration, undetected key leakage, and unauthorized re-entry of revoked participants. Traditional authentication and trust models are insufficient for fully distributed QKD topologies, which remain highly vulnerable to insider threats and persistent compromise. To address these risks, let’s propose a layered security framework composed of three integrated components: Challenge-Response Authentication (CRA), Dynamic Trust Scoring (DTS), and Blockchain-Based Access Control (BBAC). CRA verifies node legitimacy through randomized quantum-state interactions, significantly reducing impersonation and quantum replay attacks. DTS implements real-time trust evaluation using anomaly detection to dynamically downgrade compromised nodes based on their behavioral deviations. BBAC maintains an immutable and tamper-proof trust ledger to block revoked nodes from re-entering under falsified identities and resists Sybil attacks using post-quantum cryptographic primitives. Simulation results confirm that the system improves detection rates of covert threats, ensures authentication latency under 10 ms, and reduces re-entry success to zero. The proposed architecture ensures long-term scalability and resilience, making it applicable to critical domains such as finance, national infrastructure, and military communication. This work contributes a novel, verifiable, and scalable solution to one of the most pressing open problems in distributed quantum networks
Bhupinder Kaur, Deepak Prashar, Leo Mršić, Ahmad Almogren · 7 authors
Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.
Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries.
C. Aparna, S. Radha, C. Aarthi, K. M. Karthick Raghunath
ABSTRACT Mobile Ad hoc networks (MANETs) are key for applications in which flexibility and organization are paramount, but the security of such networks entails threats that can exploit the vulnerability of their open architecture, resulting in various attacks. To address such issues, a novel architectural framework is always required. One such framework is introduced, namely, the HoneyFed Secure Architecture (HFSA), which provides the combination of an advanced honey encryption system with federated learning‐based decentralized security to improve the security of MANET. Honey encryption, on the other hand, employs adaptive deception techniques to generate plausible decoy data on decryption failure, employs dynamic key management for tamper resistance, and provides perfect authentication through multi‐factor methods and zero‐knowledge proofs. We found that federated learning offers decentralized model training, where nodes jointly train local models while exchanging progress updates without exposing raw data, enabling 81.4% more detections of emerging threats while preserving data privacy. Using the proposed HFSA approach achieves a 78% protection improvement against attacks and a 71% reduction in unauthorized access. HFSA offers a robust and scalable framework of security that uses continuous learning and adaptation to the vulnerabilities of the MANETs to enhance network resilience.
Crypto wallets store and protect the private keys needed to sign transactions for crypto currencies; they are secured by multi-factor authentication schemes. However, the loss of a wallet, or a dysfunctional factor of authentication, can be catastrophic, as the keys are then lost as well as the crypto currencies. Such difficult tradeoffs between the protection of the private keys and factors of authentication that are easy to use are also present in public key infrastructures, banking cards, smartphones and smartcards. In this paper, we present protocols based on novel challenge–response pair mechanisms that protect private keys, while using factors of authentication that can be lost or misplaced without negative consequences. Examples of factors that are analyzed include passwords, tokens, wearable devices, biometry, and blockchain-based non-fungible tokens. In normal operations, the terminal device uses all factors of authentication to retrieve an ephemeral key, decrypt the private key, and finally sign a transaction. With our solution, users can download the software stack into multiple terminal devices, turning all of them into backups. We present a zero-knowledge multi-factor authentication scheme allowing the secure recovery of private keys when one of the factors is lost, such as the token. The challenge–response pair mechanisms also enable a novel key pair generation protocol in which private keys can be kept secret by the user, while a Keystore can securely authenticate the user and transmit the public key to a distributed network. The standardized LWE post-quantum cryptographic CRYSTALS Dilithium protocol was selected in the experimental section.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Mobile ad hoc networks (MANETs) facilitate data communication across multiple nodes and hop stations, characterized by their dynamic topology. This inherent flexibility, however, makes MANETs vulnerable to various security threats, notably blackhole and wormhole attacks, where malicious nodes can intercept and manipulate data. This study investigates the security vulnerabilities of MANETs, particularly against blackhole, Sybil, and wormhole attacks, and introduces the Advanced Blockchain Dynamic Source Routing (ABCD) algorithm to address these challenges. Motivated by the need for robust and decentralized security solutions in MANETs, the proposed algorithm integrates blockchain technology and homomorphic encryption to secure data communication without intermediate decryption. The ABCD algorithm leverages Dijkstra’s algorithm for optimal routing and employs a tamper-proof, decentralized data storage approach. Comparative analysis under attack scenarios reveals that the ABCD algorithm outperforms the standard DSR protocol across multiple quality of service metrics, demonstrating a significant improvement in MANET security over equivalent studies. The packet delivery rate is also improved from 81 to 92% using the modified ABCD algorithm.
Abstract Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.
Михайленко, Олександр Ігорович, Гороховський, Кирило Семенович, Гороховський, Семен Самуїлович
The paper explores the possibility of expanding the use of end-to-end encryption protocols based on the Double Ratchet algorithm in applications with low trust in the server, particularly in turn-based games and strategic interactions. The relevance of the research is due to the growing need for secure communication in cyberattacks, especially during military operations. The field of end-to-end encryption requires the study of additional applications beyond the usual ones, such as encrypted communication in text messengers. The developed implementation of the protocol can be safely used in any applications that aim to implement end-to-end encryption and satisfy the criterion of session ephemerality (in cases where secrets are stored outside a secure environment). The implemented server supports ephemeral sessions, which guarantee minimal risks of information compromise, and uses digital signatures (EdDSA) for user authentication. Logical routing of requests ensures efficient message transmission in secure scenarios. The choice of the classic game of checkers as an example allowed the authors to effectively demonstrate the advantages of end-to-end encryption and the capabilities of the implemented protocol. All cryptographic operations, including key generation, encryption and decryption of messages, are successfully performed on client devices. It is important to improve error handling mechanisms and optimize the operation of WebAssembly. An interesting area of further research is the creation of zero-knowledge proof mechanisms to prevent Man-In-The-Middle attacks during the creation of a shared secret, optimizing integration with cryptographic hardware security modules (HSM), and exploring the scalability of the solution. The proposed approach can be used to solve real-world information security problems where trust in the data transmission channel is critically important. Thus, the work has created a comprehensive solution that includes a cryptographic protocol, a backend, and a web client, which demonstrates the viability of end-to-end encryption in browser environments and multiplayer games. The work can be used as a basis for further research and development in the field of security of communication systems and privacy in multiplayer games.
Jan 1, 2025·VI All-Russian (National) Scientific Conference "Science, Technology, Society: Ecological Engineering for Sustainable Development of Territories"
A mathematical approach is proposed for evaluating the resilience of a distributed ledger of SmartDust-class sensor microsystems under conditions of cyber-physical attacks and industrial interference. A stochastic model of the trusted sensor network is developed, incorporating the probabilities of node compromise, telemetry packet loss, and data verification errors. Analytical expressions are derived for the indicators of data trustworthiness, latency, and energy reliability of the system. The simulation results demonstrate the influence of network topology parameters and attack intensity on the integral resilience index of the sensor infrastructure.
We compare the total capital efficiency of secure restaking and Proof-of-Stake (PoS) protocols. First, we consider the sufficient condition for the restaking graph to be secure. The condition implies that it is always possible to transform such a restaking graph into separate secure PoS protocols. Next, we derive two main results: upper and lower bounds on the required extra stakes to add to the validators of the secure restaking graph to be able to transform it into secure PoS protocols. In particular, we show that the restaking savings compared to PoS protocols can be very large and can asymptotically grow as a square root of the number of validators. We also study a complementary question of aggregating secure PoS protocols into a secure restaking graph and provide matching lower and upper bounds on the PoS savings.