<b><i>Governance Fork Farming</i></b> is a strategic exploitation pattern in decentralized finance (DeFi) and proof-of-stake (PoS) ecosystems where actors repeatedly engineer, anticipate, or provoke governance forks to extract economic rewards. By positioning capital, validator power, or voting rights ahead of contentious governance events, attackers harvest duplicated assets, incentives, or control advantages across forked states. This threat undermines governance legitimacy and destabilizes network continuity without violating protocol rules.
The fast growth of the Internet of Medical Things (IoMT) has made it much harder to manage and protect medical data because of security and privacy issues. This paper suggests a secure federated cloud storage system that uses a hybrid heuristic attribute-based encryption (ABE) scheme combined with a permissioned Blockchain to solve these problems. The suggested system improves data privacy and integrity by first gathering medical data and then encrypting it with ABE using the best key made by the Hybrid Mexican Axolotl with Energy Valley Optimizer (HMO-EVO). A permissioned blockchain securely stores the encrypted data, making sure that access is tightly controlled and that data breaches are avoided. The system uses federated learning with a Multi-scale Bi-Long Short-Term Memory and Gated Recurrent Unit (MBiLSTM-GRU) to make accurate predictions about diseases. This helps with healthcare monitoring. This federated approach lets deep learning models be trained in different places, keeping patient data private while still allowing for collective learning. The experimental results demonstrate that the proposed system surpasses traditional methods regarding security, efficiency, and predictive accuracy. This study presents an extensive framework for the secure management of medical data, integrating the advantages of federated learning and blockchain technology to tackle the essential challenges of data ownership, regulatory adherence, and privacy within IoMT networks.
With the rapid development of financial technology and the digital economy, fraud detection in financial transactions faces increasing challenges due to complex transaction networks, temporal dependencies, and nonlinear interactions.This study proposes an RL-LGNN framework that integrates long short-term memory (LSTM) networks, graph neural networks (GNN), and reinforcement learning (RL) for fraud detection in the financial transaction settlement process.LSTM is used to encode historical transactions as temporal sequences and extract time-dependent behavioural features.GNN then models inter-node transaction relationships and captures structural information from the transaction graph.On this basis, RL is introduced to dynamically optimise the detection strategy, thereby improving model adaptability and robustness.Experimental results on both public and real-world datasets show that the proposed framework outperforms conventional methods and achieves fraud detection accuracy above 90%.The proposed framework provides an effective solution for fraud detection in financial transaction settlement.
Existing scholarship often employs metaphors that depict platforms as fixed, bounded spaces. This paper introduces the concept of âliquid platformsâ, inspired by the metaphor of liquidity, to critically examine the fluid, layered and contested nature of contemporary digital economies. Drawing on ethnographic research and interviews with non-fungible token collectors and industry leaders based in Hong Kong, I demonstrate how these actors enact a form of âmakeshift decentralisationâ through the manipulation of fluid platform boundaries, labour-intensive âgrindingâ practices and visualisation tools, all aimed at imbuing non-fungible tokens with liquidity. Despite these efforts to manufacture price stability, platforms remain susceptible to structural liquidity corrections. The analysis highlights the increasingly important role that platforms â both as infrastructural and sociotechnical systems â play in generating liquidity. The âliquid platformâ metaphor offers a valuable framework for understanding the ever more complex, unstable dynamics that shape modern digital economies.
In Vertical Federated Learning (VFL), prior work has primarily focused on protecting data privacy, while overlooking the risk that participants may manipulate local model execution to mount integrity attacks.Integrating zero-knowledge proofs (ZKPs) into the training process can ensure that each party's computations are verifiable without revealing private data.However, directly encoding deep model training as a monolithic ZKP circuit is impractical due to: (i) complex circuit design and high overhead from frequent parameter commitments, (ii) expensive proof generation for embeddings(cross-party information interface), and (iii) synchronous proof generation that blocks iterative training rounds.To address these challenges, we present ZKSL, an efficient and asynchronous VFL framework that achieves verifiable training under a malicious threat model.ZKSL partitions deep neural networks into layer-wise circuits and generates their proofs in parallel, ensuring input-output consistency via Privacy-Commitment PLONK (PC-PLONK), a lightweight extension that supports low-cost, iteration-by-iteration parameter commitments.For embedding layers, ZKSL adopts a probabilistic verification technique that reduces proof complexity from O(N nd) to O(nd).Furthermore, ZKSL incorporates an asynchronous compute-prove scheduling mechanism to decouple proof generation from training iterations, effectively mitigating pipeline stalls.Experimental results on DeepFM and CNN models show that ZKSL reduces proof generation time by up to 73% while maintaining 99.4% accuracy, demonstrating superior scalability and practicality for real-world federated learning.
In the digital economy era, the contradiction between data sharing and privacy protection is increasingly prominent. Traditional privacy protection technologies such as anonymization, differential privacy, and homomorphic encryption are difficult to meet the diverse privacy demands in multiple fields due to their flaws such as vulnerability to cracking, sacrificing data accuracy, and high computational complexity. Zero-knowledge proof (ZKP), with its core advantage of "data available but not visible", has become the key technical support to solve this contradiction. However, the current protocol types are complex and the demands in various fields vary significantly, leading to confusion in protocol selection and imbalance in resource allocation in practice. This paper systematically reviews the theoretical basis, technical system and mainstream protocol characteristics of zero-knowledge proof, integrates the advantages of demand-driven decision tree and protocol three-dimensional classification model, constructs a protocol selection framework of "demand-driven - feature matching - dynamic optimization", and clarifies the multi-stage decision-making process; Four typical fields, namely education, machine learning, finance, and healthcare, were selected to verify the effectiveness and universality of the framework, providing standardized tools for the large-scale application of zero-knowledge proof technology in various fields and direction guidance for the development of privacy protection technology in the post-quantum era.
Alen Horvat, Steffen Schwalm, Johannes Sedlmeir, Hakan Yildiz
Among the privacy-enhancing technologies explored in the context of the European Digital Identity (EUDI) Wallets, zero-knowledge proofs stand out for their ability to maintain established levels of cryptographic verifiability while enabling data minimisation â relative to the requirements expressed by the relying party (RP). However, legal frameworks in many sectors require the collection of verifiable data beyond the RPâs immediate needs, which may substantially narrow down the scope of data minimisation that can be achieved in regulated domains. Accordingly, this paper examines the tensions between the strict data minimisation requirements for the EUDI Wallets and the extensive legal proof obligations that relying parties must fulfil. Our analysis of the regulatory foundations and relevant technical mechanisms identifies documentation, audit, and long-term preservation obligations as key sources for friction. We explore the implications of the corresponding tensions, point to gaps in current standardisation and compliance regimes, and suggest potential technical and non-technical solution approaches that could help reap the benefits advanced privacy-enhancing technologies can offer in practice.
As vehicles become increasingly intelligent, the Internet of Vehicles (IoV) is gaining widespread popularity. However, security concerns in the IoV are becoming increasingly critical, particularly regarding vehicle identity authentication, which directly affects system stability and trust. Therefore, designing an efficient and secure authentication mechanism has become an essential challenge in IoV development. This study focuses on the identity authentication problem between vehicles and roadside units (RSUs) in the IoV. To address the computational overhead challenges posed by the rapid increase in the number of vehicles, we propose a lightweight authentication scheme based on a zero-knowledge proof and design a batch verification mechanism for it. Furthermore, to address the dynamic nature of vehicular traffic, we introduce a Federated Intelligent Dynamic Batching Algorithm (FIDBA) based on a lightweight Multi-Layer Perceptron (MLP). This algorithm dynamically predicts the optimal batch size to minimize verification latency and computational cost. This approach ensures security while significantly reducing computational and communication costs, improving scalability and real-time performance. To further address delays in the handover process, we are integrating blockchain technology. Leveraging its decentralized, immutable, and transparent nature, the blockchain securely stores authenticated vehicle information, avoiding repeated verifications and reducing handover delays. To mitigate inefficiencies in blockchain queries, we design a caching mechanism that enables fast data access. Through theoretical analysis and experimental validation, this study demonstrates the superiority of the proposed scheme in security, efficiency, and scalability. This scheme meets current IoV needs and provides a promising reference for future intelligent transportation systems.
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.
Nirmala Raju Kanti, D. G. Narayan, Pooja Shettar, P S Hiremath
Blockchain networks rely on consensus mechanisms to maintain security and efficiency. Delegated Proof of Stake (DPoS) is a widely adopted alternative to traditional Proof of Work (PoW) and Proof of Stake (PoS) due to its scalability. However, DPoS suffers from centralization risks, collusion, and the presence of unreliable validators, compromising network integrity. One approach to mitigating these issues is the downgrade method, which reduces the influence of dishonest validators over time by penalizing their stake or voting power. While this approach improves security, it still faces challenges related to manipulation and inefficiency. To address these limitations more effectively, we introduce Game Theory-based Delegated Proof of Stake (GT-DPoS), an advanced consensus framework that integrates strategic decision-making through game theory to optimize node selection and incentivization. GT-DPoS utilizes a two-stage evaluation mechanism based on Reputation Score (RS) and Trust Score (TS) to regulate node behavior, penalize malicious actors, and reward honest participation. In Stage 1, nodes are assessed based on RS, incorporating factors like stake, transaction efficiency, block contribution, and past misconduct. Nodes failing to meet the threshold are penalized, while eligible ones advance to second stage. Stage 2 evaluates TS, considering rewards, penalties, and community votes, refining the selection through a payoff-based model that ensures rational decision-making. Unlike the downgrade method, GT-DPoS provides a more dynamic and adaptive approach, ensuring continuous security enhancement without long-term inefficiencies. Experimental results demonstrate that GT-DPoS achieves faster block creation times compared to conventional DPoS, with up to 4.6% improvement at lower transaction loads and 1.2% at higher loads. By leveraging game-theoretic principles, GT-DPoS enhances decentralization, reduces transaction latency, and strengthens network security, making it a more effective and performance-optimized alternative to both traditional DPoS and downgrade-based approaches
Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti, Marco Zecchini
An essential requirement for the large-scale adoption of Web3 is enabling users to benefit from their data even within already deployed systems. This raises an important open question: how can existing, widely adopted software verify that a user has retrieved specific data from a TLS server? Impressive scientific results (e.g., DECO [CCS20] and the work of Xie et al. [USENIX24]) and industrial products (TLSNotary) have recently made progress in the above challenging direction. However, while they nicely leave TLS servers untouched, the retrieved data is then used in computations with verifiers that are required to run some advanced non-standardized cryptographic schemes (e.g., ZK-SNARKs), which clearly limits the large-scale adoption of the proposed technologies. In this paper, building on top of previous approaches and relying on the recent concept of Predicate Blind Signatures of Fuchsbauer and Wolf [Eurocrypt24], we bypass the limits of prior work by presenting ACTS a distributed architecture that, while still leaving TLS servers untouched, it allows a user to show possession of data retrieved from TLS servers simply requiring that the software of the verifier can check a standard signature. Our contributions include a round-optimal predicate blind signature protocol that produces standard RSA-PSS signatures. We show how this primitive can be integrated into the DECO architecture (and its successors) to certify data retrieved from TLS servers. Furthermore, we have optimized our construction to make it practical on commodity hardware for a large and significant class of policies implemented by the notary (i.e., the actor that is in charge of obliviously certifying TLS data, therefore preserving data confidentiality). We provide an experimental evaluation on the simple but powerful enough use case of a PDF document downloaded from a TLS server and encoded into an AES-GCM ciphertext. The user will then get a certified PDF through a standard PADES signature added obliviously to the PDF along with some metadata by a notary service. The resulting standard signed PDF document can be transparently verified using off-the-shelf PDF readers. Our experimental validation demonstrates that our architecture is suitable for real-world deployment in concrete scenarios.
Ahmet Ramazan AÄÄąrtaĹ, Arda BuÄra Ăzer, ZĂźlfĂźkar SAYGI, OÄuz Yayla
Unbiased and unpredictable randomness is a cornerstone of Web3 security, underpinning everything from consensus protocols to DeFi logic. Although Distributed Verifiable Random Functions (DVRFs) eliminate central points of failure, current designs often have to compromise performance. Most existing protocols are hindered by one of three limitations: proofs that scale linearly with the number of participants, high computational cost of bilinear pairings, or latency introduced by mandatory interactive steps during generation. In this work, we present Icy-DVRF, a protocol that improves DVRFwCP by employing a preprocessing scheme similar to FROST to reduce the number of interaction rounds among participants and lowering the additional communication cost from <inline-formula> <tex-math notation="LaTeX">$O(n^{2} t)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(t)$ </tex-math></inline-formula> while maintaining constant-size proofs. The downside of our construction is that, relative to DDH-DVRF and GLOW-DVRF, this approach incurs an additional off-chain communication round due to the threshold structure of our non-interactive zero-knowledge proof. This architecture ensures that verification costs remain low, regardless of the set of participants. While theoretical estimates suggest verification costs of approximately one quarter of those of standard designs, our empirical benchmarks on the Sepolia testnet, utilizing the EIP-2537: Precompile for BLS12-381 curve operations, confirm that Icy-DVRF requires only 88,803 gas for full execution. This represents a significant 43.02% reduction in total gas consumption compared to existing pairing-based constructions, saving 67,035 gas per on-chain verification. Off-chain, eliminating DVRFwCP’s Augmented Secure-DKG round yields a per-node speedup ranging from a factor of 1.46 at <inline-formula> <tex-math notation="LaTeX">$(n,t)=(5,3)$ </tex-math></inline-formula> to a factor of 4.43 at <inline-formula> <tex-math notation="LaTeX">$(n,t)=(50,34)$ </tex-math></inline-formula>.
Open access
Generative Adversarial Networks and Image Synthesis