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Karam Eddine Bilami, Jaafar Gaber, Pascal Lorenz, Azeddine Bilami
No abstract is available for this record.
Jorge Pumar
We present the Lindblad Protocol, a distributed consensus mechanism grounded in physical verification rather than computational proof-of-work or proof-of-stake. The protocol treats distributed network state as a continuously evolving density operator governed by the Lindblad master equation for open dissipative quantum systems. Trust is established through the Lindblad Cryptography Protocol (LCP), a four-layer physical verification stack: hardware identity via silicon Physical Unclonable Functions (SRAM PUF), cryptographic signing via P-256 ECDSA derived from PUF output, spatiotemporal entropy via the Hybrid Stochastic Chua circuit (HSC), and irreversible consensus via the Lindblad master equation over a dissipative LoRa mesh network. In existing consensus mechanisms, security guarantees are computational and are therefore bounded by adversarial compute resources. LCP anchors security in thermodynamic law: a recorded state transition cannot be reversed without violating the second law of thermodynamics. The protocol simultaneously proves what was signed, when, where, and who, without a trusted third party. Hardware validation on commodity Heltec ESP32-S3 nodes demonstrates SRAM PUF inter-device Hamming distance of 48.60% (intra-device: 0.00%), a 486× separation ratio confirming strong uniqueness. A fuzzy extractor based on BCH(255,139,t=15) achieves 86% rock-stable bit selection across 12 power-cycle enrollment, validated on physical hardware with 100% reproduction fidelity. The protocol is deployed on mainnet: a live network of physical hardware nodes has produced over 21,000 blocks across 35,842 epochs, mining over 1,512,000 PYCO tokens via Physical Coherence Verification (PCV-4), with a bridge operating across Arbitrum One and Polygon providing USDT/USDC settlement. The first peer-to-peer transfer between two real users was completed on May 29, 2026.
Paolo Penna, Manvir Schneider
Many proof-of-stake protocols finance validator rewards from two sources: transaction fees and a finite reserve of tokens. This creates a dynamic hand-off problem. Early in the life of the system, fees may be too small to fund the target level of security; later, fees may become sufficient. The central question is whether the reserve provides enough runway for the protocol to remain secure until this fee-only region is reached. We study this problem in a discrete-time stochastic model of validator participation. Token price and transaction demand fluctuate over time, while validators choose participation strategically. We solve the validator entry game and derive an exact state-dependent reserve threshold, i.e., the minimal reserve stock necessary and sufficient to sustain a target security level. This threshold separates three regions: infeasibility, reserve-dependent security, and fee-only security. Security fails if the reserve first falls below the state-dependent threshold, and a successful hand-off occurs exactly if the fee-only region is reached before that failure time. We derive stress-test guarantees that convert lower confidence bands for token price and demand into reserve requirements, and obtain explicit failure-probability and expected hand-off-time bounds. Finally, we extend the model to forward-looking validators and derive the Markov participation condition that captures how current participation affects future reserve-funded rewards. The main implication is that reserve policy should not be evaluated by nominal depletion dates or steady-state reward ratios alone. A protocol can have a large nominal reserve and still be close to security failure after adverse price or demand shocks. Conversely, once demand crosses the fee-only threshold, the reserve becomes redundant for security. This paper provides a tractable equilibrium framework for stress-testing this transition.
Jianfeng Xu
We study lossy compression of a finite statement source generated in a fixed deductive environment. The source symbols are statements in a knowledge base endowed with a shared proof system, and reconstruction fidelity is measured by preservation of deductive closure rather than by symbolwise equality. Fixing the proof system and a canonical scan order yields a decomposition of the source alphabet into an irredundant core and redundant stored consequences. At zero distortion, each core symbol induces a set of distortion-free reconstructions. In the nonconfusable (disjoint-core) regime, we show that the minimum zero-distortion rate equals the source mass of the core times the entropy of the source conditioned on that core. In the general confusable-core regime, we characterise the exact zero-distortion rate via a hypergraph-entropy quantity induced by jointly realisable core subsets, with a reduction to Korner-style graph entropy under a natural pairwise realisability condition. For reconstruction alphabets contained in the deductive closure of the source knowledge base, we further prove that the full rate-distortion function depends only on the core, so redundant states are invisible to both rate and distortion. Finally, when the decoder is limited to a bounded inference-depth budget (a bounded number of iterations of the immediate-consequence operator), we obtain an exact rate-depth-distortion characterisation. Under an additional order-robustness assumption identifying the chosen core with the order-free essential set, this characterisation interpolates between classical symbolwise compression and unconstrained deductive compression.
Jianfeng Xu
Shannon's rate-distortion theory treats source symbols as unstructured labels. When the source is a knowledge base equipped with a logical proof system, a natural fidelity criterion is closure fidelity: a reconstruction is acceptable if it preserves the deductive closure of the original. This paper develops a rate-distortion theory under this criterion. Central to the theory is the irredundant core-a canonical generating set extracted by a fixed-order deletion procedure, from which the full deductive closure can be rederived. We prove that the zero-distortion semantic rate equals a quantity that is strictly below the classical entropy rate whenever the knowledge base contains redundant states. More generally, the full semantic rate-distortion function depends only on the core; redundant states are invisible to both rate and distortion. We derive a semantic source-channel separation theorem showing a semantic leverage phenomenon: under closure fidelity, the required source rate is reduced by an asymptotic leverage factor greater than one, allowing the same knowledge base to be communicated with proportionally fewer channel uses-not by violating Shannon capacity, but because redundant states become free. We also prove a strengthened Fano inequality that exploits core structure. For heterogeneous multi-agent communication, an overlap decomposition gives necessary and sufficient conditions for closure-reliable transmission and identifies a semantic bottleneck in broadcast settings that persists even over noiseless channels. All results are verified on Datalog instances with up to 24,000 base facts.
Wenqi Li, Zijie Pan, Yufeng Yang
Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive balance information, leak structural features of payment routes, and enlarge the attack surface for probing, channel exhaustion, and selective forwarding. This paper presents a novel framework, Adaptive Multipath Proofs (AMPs), for privacy protection and security in payment channel networks. The core idea is to bind multipath routing decisions with lightweight zero-knowledge verifiability, allowing intermediate nodes to validate path feasibility, fragment consistency, and settlement constraints without learning exact channel balances, the complete payment amount, or the global route structure. AMP integrates three mechanisms: a hidden-liquidity feasibility proof that supports privacy-preserving route selection, an adaptive payment-splitting strategy that dynamically determines fragment allocation according to network congestion and balance uncertainty, and a proof-coupled settlement guard that enforces atomicity and timeout consistency across all payment fragments. Together, these mechanisms reduce information leakage while preserving robust payment execution under dynamic network conditions. Experimental evaluation on real Lightning Network topologies and synthetic stress scenarios demonstrates that AMP significantly lowers balance disclosure and endpoint inference risk, improves payment completion under skewed liquidity distributions, and introduces only moderate computational and communication overhead. The results indicate that adaptive proof-carrying multipath routing offers a practical and effective direction for building secure, privacy-preserving, and high-success payment channel networks.
Alex Shvets
We prove that Jensen–Shannon divergence (JSD) contraction coefficients exhibit universal strict super-tensorization: for every finite channel W with nontrivial contraction 0 < η_JSD(W) < 1, one has η_JSD(W⊗2) > η_JSD(W). The sequence η_n(W) := η_JSD(W⊗n) is nondecreasing, strictly increases along doubling, and satisfies lim η_n(W) = 1, while for η_JSD(W) ∈ {0, 1} it is identically 0 or 1. This contrasts sharply with the multiplicative tensorization η_f(W⊗n) = η_f(W)^n enjoyed by operator-convex f-divergences (KL, χ², squared Hellinger), for which contraction decays exponentially to zero. To our knowledge, this is the first f-divergence for which a universal strict super-tensorization law is established. The proof uses the Ordentlich–Polyanskiy binary edge reduction, expresses the binary JSD SDPI constant as a normalized posterior-variance functional, and shows strict amplification via the law of total variance. Convergence rate is controlled by the Bhattacharyya coefficient: 1 − η_n(W) ≤ 2A^n. Numerical verification over 4729 random channels across 26 configurations confirms zero violations. **Update v1.1:** Includes addendum with three targeted clarifications: (1) precise assumptions for binary edge reduction lemma replacing informal "mild regularity conditions," (2) explicit two-case split in the key strictness argument (Lemma 5.2, Step 2), (3) refined table caption for operator-convex divergences.
Ahmed Lateef Salih Al-Karawi, Rafet Akdeniz
The fifth-generation (5G) networks are facing critical security challenges in device authenti- cation for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency, communication over- head, and the energy consumption impractical for resource-constrained 5G devices. This paper proposes zero-knowledge federated learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates where each device generates a proof Proofi = ZK(Gradienti, Hashi), demon- strating computational integrity.Experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 KB versus 512 KB (99.97% reduction), and energy consumption of 284.5 mJ versus 6.525 mJ (95% reduc- tion), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment.
John M. Owen
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.
Ramesh Kumar, Joy Dutta, M. Vamsi, Uma Sankararao Varri · 5 authors
The integration of Artificial Intelligence (AI) into sixth-generation (6G) networks is a foundational requirement for achieving unprecedented performance, but it also introduces a sophisticated threat landscape that legacy security frameworks cannot address. This paper presents a comprehensive review of this dual role of AI, analyzing its potential to both compromise and safeguard future networks. Since AI has the ability to both protect and compromise security and privacy, its implementation with 6G technology may sometimes be a double-edged sword. The primary objective of this survey is to systematically analyze existing research that integrates AI techniques into 6G architectures, focusing on their implications for security and privacy. Among the concerns being investigated is the fundamental privacy and security risk associated with 6G technologies. Therefore, in order to incorporate and confirm this foundational research as a platform for future research, we have developed a review on the specifics of 6G security and privacy. The methodology involves reviewing recent academic and industrial studies related to AI-enabled 6G frameworks, threat models, and defense mechanisms, with an emphasis on how AI contributes to intrusion detection, authentication, and privacy preservation. This paper begins with a historical analysis of previous networking technologies and how they impacted contemporary 6G networking improvements. Therefore, this article discusses extensively the aspects that have rendered 6G technology relevant as well as the ongoing 6G-based projects. In addition, it identifies and critically evaluates key enabling technologies, including distributed ledger technology (DLT/blockchain), physical layer security (PLS), terahertz (THz) communication, quantum computing, visible light communication (VLC), and distributed AI/ML, that underpin secure 6G environments. The paper concludes by summarizing open challenges, future research opportunities, and potential pathways for building trustworthy AI-driven 6G systems.
Yuhang Yao, Syed A. Jafar
An open question recently posed by Fawzi and Ferme [IEEE Transactions on Information Theory 2024], asks whether non-signaling (NS) assistance can increase the capacity of a broadcast channel (BC). We answer this question in the affirmative, by showing that for a certainK-receiver BC model, called Coordinated Multipoint broadcast (CoMP BC) that arises naturally in wireless networks, NS-assistance provides multiplicative gains in both capacity and degrees of freedom (DoF), even achievingK-fold improvements in extremal cases. Somewhat surprisingly, this is shown to be true even for 2-receiver broadcast channels that are semi-deterministic and/or degraded. In a CoMP BC,Bsingle-antenna transmitters, supported by a backhaul that allows them to share data, act as oneB-antenna transmitter, to send independent messages toKreceivers, each equipped with a single receive antenna. A fixed and globally known connectivity matrix specifies for each transmit antenna, the subset of receivers that are connected to (have a non-zero channel coefficient to) that antenna. Besides the connectivity, there is no channel state information at the transmitter. The receivers have perfect channel knowledge. We show that NS-assistance has no DoF advantage in a fully connected CoMP BC. The DoF region is fully characterized for a class of connectivity patterns associated with tree graphs, for which the classical sum-DoF value is shown to be the number of leaf nodes, while the NS-assisted sum-DoF value is the total number of all (non-root) nodes. For arbitrary connectivity patterns, the sum-capacity with NS-assistance is bounded above and below by the min-rank and triangle number of the connectivity matrix, respectively, leading to matching bounds in many cases, e.g., if min(B,K) ≤ 6. While translations to Gaussian settings are demonstrated, for simplicity most of our results are presented under noise-free, finite-field (Fq) models. Converse proofs for classical DoF are found by adapting the Aligned Images bounds to the finite field model. Converse bounds for NS-assisted DoF/capacity extend the same-marginals property to the BC with NS-assistance available to all parties. Beyond the BC setting, even stronger (unbounded) gains in capacity due to NS-assistance are established for certain ‘communication with side-information’ settings, such as the fading dirty paper channel.
Antoine Douteau, Adeline Roux-Langlois
No abstract is available for this record.
IGOR, CHECHELNITSKY
This work proves a formal impossibility theorem stating that no observable behavioral or biometric signal can serve as a cryptographic secret under standard semantic security notions (IND-CPA / IND-CCA), in any computational model admitting machine learning approximation and side-channel observability. The result holds in classical, post-quantum, and hybrid adversarial models. We further derive strict architectural consequences for biometric authentication, fuzzy extractors, and behavioral identification systems, showing that such signals may only function as zero-knowledge liveness proofs, not as entropy sources for cryptographic key material.
Flavien Dermigny, Vania Conan, Samia Bouzefrane, Pengwenlong Gu · 6 authors
In the domain of authentication, information leakage which can lead to identity theft represents a significant challenge in the field of cybersecurity. This challenge is particularly relevant in the context of 5 G tactical bubbles, where secure and efficient authentication mechanisms are critical to gain access to sensitive information and communication services. The concept of Zero-Knowledge Proofs, in particular non-interactive proofs, has gained attention in recent years as robust cryptographic methods for privacy-preserving protocols. Zero-knowledge proofs enable users to prove possession of specific knowledge to verifiers without revealing the knowledge itself in a single interaction round. Despite its growing popularity, Zero-Knowledge Proofs have not yet been fully explored within 5 G tactical bubbles. In this paper, we perform a comparative analysis between traditional authentication mechanisms and Zero-Knowledge Proofs-enabled authentications. To this end, we evaluate the feasibility in terms of time and computational complexity and determine whether these advanced authentication protocols can ensure enhanced privacy and security in 5 G tactical bubbles.
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.
Mamoon M. Saeed, Rashid A. Saeed, Mohammad Kamrul Hasan, Elmustafa Sayed Ali · 8 authors
After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations.
Vidas Sileikis, Wei Wang
Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems.
Fengqun Wang, Jie Cui, Wuquan Wen, Ke Hu
Physical unclonable function (PUF) is a critical hardware primitive that provides unique identities for authenticating a large number of devices in the Industrial Internet of Things (IIoT). Most existing PUF-based schemes face challenge-response pair (CRP) leakage during machine-learning attack. Some studies that use hardware or time-consuming cryptographic operations to protect the PUF responses are expensive and unsuitable for existing IIoT devices. To address these issues, a lightweight and anonymous PUF-based authentication scheme is proposed for resource-constrained IIoTs. Using elliptic curve cryptography and zero-knowledge proof, a lightweight blinding mechanism is designed in the proposed scheme that prevents explicit CRP leakage and ensures anonymity. In addition, the authenticated keys are random with forward and backward secrecy. Moreover, the security of the proposed scheme is demonstrated using a random oracle model. Experimental results demonstrate that the proposed scheme is notably more efficient and practical for resource-constrained devices compared to other related schemes.
Yubo Song, Yuxin Lin, Hongyu Zhu, Han Zhao · 8 authors
No abstract is available for this record.
Dadmehr Rahbari, Masoud Daneshtalab, Maksim Jenihhin
With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized training. To tackle the typical challenges of FL—such as high communication overhead, resource limitations, and convergence inefficiencies—the method integrates dynamic client clustering, quantization-aware training, and structured model pruning. These optimizations collectively reduce latency and memory consumption while accelerating model convergence. Evaluations using the Human Activity Recognition dataset show that the proposed approach outperforms several state-of-the-art FL methods, achieving an average +8.4% improvement in accuracy, -16.2% lower latency, -35.1% reduction in memory usage, and -2.7% lower security overhead. These results demonstrate its suitability for real-time, resource-constrained scenarios in domains like healthcare, IoT, and finance, where maintaining a strong balance between efficiency and privacy is essential.
Asim Ul Haq, Seyed Salar Sefati, Syed Junaid Nawaz, Albena Mihovska · 5 authors
Recent revolutionary advancements in the services as observed with the use cases of Industry 5.0, consumer electronics 2.0/smart devices 2.0, digital healthcare ecosystem, Internet-of-Things (IoT), advanced digital finance/currency, and Non-Terrestrial Network (NTN) expansion, to name a few, have resulted in a spectacular growth in the number of wireless-connected devices. Subsequently, this has drastically increased the demands for network capacity, channel capacity, reliability, privacy, and security provisions. Despite that, the 5 th Generation (5G) of wireless communication networks has introduced various innovative services such as Ultra-Reliable Low Latency Communication (URLLCs), Massive Machine Type Communication (mMTCs), and Enhanced Mobile Broadband (eMBB). These services only support isolated operations and the requisite reliable service delivery remains a challenge. The Beyond 5G (B5G)/6 th Generation (6G) wireless networks aim at simultaneously providing multiple integrated services through intelligent network operations with ultra-high speed and reliability supporting integrated NTN and terrestrial networks. However, the prospect of such an extensively connected decentralized 3D wireless network also foresees security concerns, underscoring the necessity for seamless and infrastructurefree (decentralized) security solutions. The conventional security mechanisms are considered inadequate to ensure the security provisions of such extensive, decentralized, and heterogeneous networks. Physical Layer Security (PLS) is a promising technique to extend seamless and infrastructure-less security solutions, ensuring the availability, confidentiality, and integrity of legitimate transmissions. This paper provides a comprehensive overview with tutorials and presents the state-of-the-art of PLS, focusing mainly on NTN wireless communications. Furthermore, current research challenges, open issues, and future research directions are also thoroughly discussed in an amalgamation of various emerging 6G technologies. Finally, we provide an overview of implementation challenges in NTN and potential solutions to support the standardization progression of NTN in upcoming releases of 3 rd Generation Partnership Project (3GPP).
Chuan Ma, Siyang Jiang, Hao Yang, Qipeng Xie · 7 authors
In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across multiple institutions while maintaining data decentralization. Despite its advantages, FL is vulnerable to adversarial threats, particularly poisoning attacks during model aggregation, a process typically managed by a central server. However, in these systems, neural network models still possess the capacity to inadvertently memorize and potentially expose individual training instances. This presents a significant privacy risk, as attackers could reconstruct private data by leveraging the information contained in the model itself. Existing solutions fall short of providing a viable, privacy-preserving BRFL system that is both completely secure against information leakage and computationally efficient. To address these concerns, we propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.
Matheus V. X. Ferreira, Aadityan Ganesh, Jack Hourigan, Hannah Huh · 6 authors
Cryptographic Self-Selection is a paradigm employed by modern Proof-of-Stake consensus protocols to select a block-proposing "leader." Algorand [Chen and Micali, 2019] proposes a canonical protocol, and Ferreira et al. [2022] establish bounds $f(α,β)$ on the maximum fraction of rounds a strategic player can lead as a function of their stake $α$ and a network connectivity parameter $β$. While both their lower and upper bounds are non-trivial, there is a substantial gap between them (for example, they establish $f(10\%,1) \in [10.08\%, 21.12\%]$), leaving open the question of how significant of a concern these manipulations are. We develop computational methods to provably nail $f(α,β)$ for any desired $(α,β)$ up to arbitrary precision, and implement our method on a wide range of parameters (for example, we confirm $f(10\%,1) \in [10.08\%, 10.15\%]$). Methodologically, estimating $f(α,β)$ can be phrased as estimating to high precision the value of a Markov Decision Process whose states are countably-long lists of real numbers. Our methodological contributions involve (a) reformulating the question instead as computing to high precision the expected value of a distribution that is a fixed-point of a non-linear sampling operator, and (b) provably bounding the error induced by various truncations and sampling estimations of this distribution (which appears intractable to solve in closed form). One technical challenge, for example, is that natural sampling-based estimates of the mean of our target distribution are \emph{not} unbiased estimators, and therefore our methods necessarily go beyond claiming sufficiently-many samples to be close to the mean.
Muhammad Rizwan, Mudassir Ali, Ammar Hawbani, Xingfu Wang · 8 authors
Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.