Maria Nuțu, Giorgi Akhalaia, Răzvan Bocu, Maksim Iavich
Commitment schemes represent foundational cryptographic primitives enabling secure verification protocols across diverse applications, from blockchain systems to zero-knowledge proofs. This paper presents a systematic survey of vector, polynomial, and functional commitment schemes, analyzing their evolution from classical constructions to post-quantum secure alternatives. We examine the strengths and limitations of RSA-based, Diffie–Hellman, and lattice-based approaches, highlighting the critical shift toward quantum-resistant designs necessitated by emerging computational threats. The survey reveals that while lattice-based schemes (particularly those using the Short Integer Solution problem) offer promising security guarantees, they face practical challenges in proof size and verification efficiency. Functional commitments emerge as a powerful generalization, though their adoption is constrained by computational overhead and setup requirements. Key findings identify persistent gaps in adaptive security, composability, and real-world deployment, while proposed solutions emphasize optimization techniques and hybrid approaches. By synthesizing over 90 research works, this paper provides both a comprehensive reference for cryptographic researchers and a roadmap for future developments in commitment schemes, particularly in addressing the urgent demands of post-quantum cryptography and decentralized systems.
The rapid advancement of quantum computing poses significant challenges to conventional cryptography, necessitating the adoption of post-quantum cryptography (PQC) solutions. This chapter proposes a Post-Quantum Lattice Security (PQLS) system for protecting power plant data in smart cities. It integrates Kyber for secure key exchange, Falcon for quantum-resistant digital signatures, ZKP for efficient authentication without revealing sensitive data, and JSON-LD for standardizing the format of data received from different smart meters. To evaluate the security of the proposed framework, we analyze its resistance to various threats, such as side-channel and message recovery attacks. We measured key performance indicators. The results showed an average CPU utilization of 2.4592 MS, memory consumption averaging 1843.899 KB, an execution time of 2.45 MS, and a level averaging 66.27677. This demonstrates that our proposed system offers high security and efficiency, making it a practical solution for protecting electrical infrastructure in smart cities in the quantum era.
Paper define innovative approach to unify authentication across Web2 and Web3 ecosystems by using biometric-driven decentralized identifiers (DIDs). The framework employs zero-knowledge attestations (ZKPs) to ensure privacy during verification processes [7], [11] and utilizes Chainlink's Cross-Chain protocol related toInteroperability(CCIP) for flawless operation across multiple blockchains [17]. To enhance liveness detection, we incorporate federated learning to eliminate centralized storage of sensitive biometric data [19]. A novel contribution is the Biometric Soulbound Token (BST), a non-transferable NFT that securely stores hashed facial data [5]. Also, quantum-resistant ZKPs are used to verify biometric matches without exposing raw inputs [14]. The DIDs function cohesively across Ethereum, Polygon, and Solana. Experimental results demon- strate a 99.2% authentication accuracy, a 1.3 -second latency, and full compliance with GDPR. By empowering users with control over their biometric data, this framework bridges centralized and decentralized platforms, enabling secure and efficient identity management.
This chapter proposes a Trustworthy Federated Identity Management (TFIM) framework developed to overcome user identity management challenges across multiple blockchain networks. The research work focuses on solving key issues, such as the lack of unified trust frameworks, insufficient cross-chain identity verification, and limited privacy-preserving mechanisms for cross-chain data sharing. TFIM allows secure DApp interoperability by assessing participant trustworthiness across blockchain networks with the integration of zero-knowledge proofs and federated learning techniques. Performance evaluation shows that TFIM processes 44,041 transactions/s with 15% degradation under real-world conditions, supports 1000 concurrent users, and handles 100 cross-chain authentications/s across up to 15 interconnected networks. Although TFIM provides better privacy protection and more advanced cross-chain capabilities than the Blockchain-Based Federated Identity Framework (BFIF), it comes at the cost of computational overhead and registration/authorization speed. The results suggest potential directions for future optimization while maintaining TFIM’s robust cross-chain functionality and privacy features.
The rapid growth of Internet of Things (IoT) devices has revolutionized digital ecosystems, enabling real-time automation and decision-making. However, the large volume and sensitivity of data from distributed IoT networks pose serious challenges in ensuring secure, scalable, and trustworthy data sharing. Centralized architectures are prone to failures, breaches, and limited auditability. This paper proposes a blockchain-integrated framework to overcome these limitations in heterogeneous IoT environments. The model utilizes distributed ledger technology (DLT) to ensure tamper-proof data integrity, decentralized control, and verifiable audit trails. Smart contracts dynamically enforce access policies, while attribute-based encryption (ABE) provides fine-grained control over data access. Lightweight blockchain nodes are deployed at the edge to reduce latency and reliance on cloud infrastructure. A layered architecture ensures seamless interaction among IoT devices, edge nodes, and cloud systems. To evaluate the framework, a prototype was built using ESP32 sensors, Raspberry Pi 4 edge nodes, and a Hyperledger Fabric blockchain network. Data encryption and smart contract execution were tested under realistic conditions. Results show enhanced data security, access control, and system scalability with minimal performance overhead. This framework provides a practical solution for secure data sharing in IoT ecosystems and is well-suited for deployment in smart cities, healthcare, and industrial automation.
Zeineb Ben Sassi, Chiheb Chahine Yaici, Jiahui Xiang, Osman Salem · 5 authors
Zero-Knowledge Proof (ZKP) protocols offer a powerful foundation for privacy-preserving authentication by allowing one party to prove knowledge of a secret without revealing it. Such protocols are increasingly relevant in domains such as secure communications, blockchain technologies, digital identity management, and e-health, where data confidentiality and integrity are critical. While various ZKP schemes exist, their practical performance remains a key factor in choosing the appropriate protocol for real-world applications, since efficiency directly impacts scalability, user experience, and system adoption.In this work, we conduct a comparative benchmarking study of five no table ZKP-based authentication protocols: Fiat–Shamir, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK), Zero-Knowledge Scalable Transparent Argument of Knowledge, (zk-STARK), Schnorr, and Guillou–Quisquater. Each protocol is evaluated in a standardized virtualized environment to ensure fair comparisons across implementations. We measure and analyze multiple performance metrics, including prover and verifier execution time, Central Processing Unit (CPU) and memory consumption, and network usage per proof. Our results reveal significant differences in resource efficiency, highlighting trade-offs between computational cost, proof size, and cryptographic expressiveness.This study provides a systematic evaluation clarifying the relative strengths and weaknesses of widely used ZKP protocols, serving as a practical reference for researchers, practitioners, and system designers seeking to integrate zero-knowledge techniques under real-world performance constraints.
Aug 24, 2025·Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, 2026
Thomas Gassmann, Stefanos Chaliasos, Thodoris Sotiropoulos, Zhendong Su
Zero-knowledge proofs (ZKPs) are the cornerstone of programmable cryptography. They enable (1) privacy-preserving and verifiable computation across blockchains, and (2) an expanding range of off-chain applications such as credential schemes. Zero-knowledge virtual machines (zkVMs) lower the barrier by turning ZKPs into a drop-in backend for standard compilation pipelines. This lets developers write proof-generating programs in conventional languages (e.g., Rust or C++) instead of hand-crafting arithmetic circuits. However, these VMs inherit compiler infrastructures tuned for traditional architectures rather than for proof systems. In particular, standard compiler optimizations assume features that are absent in zkVMs, including cache locality, branch prediction, or instruction-level parallelism. Therefore, their impact on proof generation is questionable. We present the first systematic study of the impact of compiler optimizations on zkVMs. We evaluate 64 LLVM passes, six standard optimization levels, and an unoptimized baseline across 58 benchmarks on two RISC-V-based zkVMs (RISC Zero and SP1). While standard LLVM optimization levels do improve zkVM performance (over 40\%), their impact is far smaller than on traditional CPUs, since their decisions rely on hardware features rather than proof constraints. Guided by a fine-grained pass-level analysis, we~\emph{slightly} refine a small set of LLVM passes to be zkVM-aware, improving zkVM execution time by up to 45\% (average +4.6\% on RISC Zero, +1\% on SP1) and achieving consistent proving-time gains. Our work highlights the potential of compiler-level optimizations for zkVM performance and opens new direction for zkVM-specific passes, backends, and superoptimizers.
The dawn of the disruptive quantum computing scenario marks a serious threat to the existence of traditional cryptosystems. With laws such as Shor’s, capable of factoring large integers in polynomial time, and Grover’s, able to speed up brute-force key searches, these attacks make conventional public-key infrastructures increasingly vulnerable, whereas even symmetric ciphers lose good measure of their strength. In this article, we focus on an elaborative description of a patented method for quantum-secure key generation, wherein Qabbalah (QBLH) complexity is utilized in the geometric-symbolic realm, in conjunction with magic number squares, phi/pi coordinate weighting, and tetrahedral trinary state encoding. The proposed system of TriGate QBLH Quantum-Safe Encryption converts seed inputs to multidimensional keys that resist linear algebraic attacks owing to non-linear permutations, irrational constant weighting, and topological complexity. Normally, pseudo-random number generators spatialize entropy in Euclidean geometry, as opposed to the present technique that places entropy in a completely non-Euclidean domain, where classical as well as quantum adversaries find it hard to traverse. We describe the method in detail, present its benefits over lattice- and hash-based post-quantum schemes, and walk through an example of its implementation. Consideration is also given to its potential integration with PQC standards, blockchain authentication, and decentralized finance applications. The system fuses symbolic mathematics, such as the 231 Gates of QBLH, with trinary logic mapped onto tetrahedral states to not only create encryption keys but also verifiable geometric signatures. This represents a paradigm shift toward geometric cryptography, which may be a viable method to realize scalable and trustworthy digital infrastructure in a quantum-threatened environment.
The fact that data generated in distributed sources grows exponentially poses great hurdles on the centralized machine learning workflow, especially regarding data privacy, latency and bandwidth overhead. Federated Feature Stores (FFS)Constitute a new paradigm that perpetrates real-time learning in which data is not aggregated. In this paper, one such architecture is provided in which feature engineering, storage and access, are decentralized and co-located with data sources taking advantage of the principles of federated learning. To satisfy the need to maintain data locality but guarantee consistency, low latency inference and privacy regulations, FFS uses a combination of on-device feature computation combined with updating global models. We are working on federated feature synchronization, version control and optimal caching for heterogeneous environments. The experimental analysis over edge clusters and cloud back ends reveals significant bumps in the end-to-end training throughput, inference latency, and privacy preservation over conventional centralized feature pipelines. The given FFS framework provides a scalable, privacy-sensitive, and efficient replacement of older data engineering pipes in the fields of healthcare, finance and the Internet of Things.
The purpose of Internet of Things (IoT) security is to ensure the availability, confidentiality, and integrity of IoT networks. However, due to the heterogeneity of IoT devices and the possibility of attacks of various kinds from both inside and outside the network, securing an IoT network is a difficult task. Handshake protocols are useful for achieving mutual authentication, which allows secure inclusion of devices into the network. By verifying that the information they receive is accurate and from a trusted source, mutual authentication minimizes the possibility that a malicious actor will compromise their connections. However, handshake protocols do not protect devices from attackers in the network. Use of autonomous anomaly detection and blacklisting prevents nodes with anomalous behavior from joining, re-joining, or remaining in the network. Similarly, trust scoring is another popular method that can be used to increase the resilience of the network against trust based system attacks. In view of the above, the contributions of this paper are three-fold. First, to ensure the security of the IoT network from outsider attacks in a zero-trust environment, we propose a new handshake protocol based on Physical Unclonable Functions that can be used in IoT device discovery and mutual authentication between the IoT device and the server. The proposed protocol is resilient to Man-in-the-Middle, replay and forgery attacks, as proven in our security analysis. Secondly, we propose a real-time intrusion and anomaly detection framework based on machine learning to prevent network-based attacks from insiders. Finally, we propose a trust system which utilizes feedback mechanisms based on smart contracts for managing the trust of a dynamic IoT network to increase resilience against behavioral attacks. Simulation results show that by using blacklisting, our trust management model provides greater resilience against trust-based attacks compared to similar blockchain-based trust models in the literature, and the proposed distributed IoT network security framework can secure an IoT network from both internal and external attacks, even in an environment where half of the devices in the network are compromised.
The increasing use of federated learning (FL) in healthcare IoT demands rigorous verification to ensure the correctness of remote model training without compromising patient data privacy. However, existing approaches either assume full trust in clients or introduce high computational and communication costs when integrating cryptographic guarantees. In this work, we propose a lightweight, privacy-preserving federated learning framework that integrates zk-SNARK-based verifiable training over a ring topology. Our system ensures that each client’s model update and aggregation step can be independently verified without revealing sensitive data or requiring a central auditor. We design an efficient proof composition strategy (CGro16) tailored for chained convolution operations and commitment schemes optimized for healthcare models. We also introduce a matrix polynomial-based masking mechanism (MatProofs) to support zero-knowledge commitments for convolutional neural networks (CNNs).Experimental results on standard benchmarks (MNIST, CIFAR-100) show up to 47% reduction in proof generation time and 39% lower memory overhead compared to baseline zk-SNARK schemes. The protocol is also benchmarked on edge devices (Jetson Nano, Raspberry Pi), confirming its suitability for remote and wearable healthcare scenarios.
Gayatri M Bhandari, Nitin M Shivale, Shrishail S Patil, Pranav Prajapati · 7 authors
Federated learning is an emerging technology that can revolutionize the training of machine learning models. Federated learning refers to an approach to training a machine learning model in a decentralized and collaborative fashion. A central server distributes the model to client devices, where it is trained locally using the clients’ own data. The client then sends the updated model weights to the server, which aggregates them to update the global model. This paper introduces a federated learning platform designed to enable collaborative training of machine learning models across multiple client devices while preserving data privacy. The platform supports a range of supervised learning algorithms, including convolutional neural networks and decision trees, and is compatible with widely used frameworks such as TensorFlow, PyTorch, and Flower. It offers a user-friendly interface where model developers can upload or deploy their machine learning models to a central server. Clients can then access these models and train them locally using their own data. The platform's modular design ensures flexibility in deployment and efficiency in handling real-world applications. The key features of this application include a model repository, secure API access for client integration, local model training capabilities on user-end devices, and a user-friendly UI. The platform aims to democratize machine learning by enabling distributed model training and deployment, promoting collaboration and efficiency across diverse use cases. The scalable infrastructure supports real-time inference, on-device training, and secure data handling, making it ideal for industries ranging from healthcare to finance and beyond.
This paper proposes and experimentally validates a holistic security framework for distributed systems, combining blockchain-based passport identity verification with AI-driven dynamic trust management. The framework addresses two critical challenges in decentralized environments: ensuring verifiable digital identities and maintaining scalable, adaptive trust evaluation. In the identity layer, electronic passports are used to generate zero-knowledge proofs, allowing users to demonstrate specific attributes without exposing sensitive personal information. This mechanism provides strong Sybil resistance and aligns with Self-Sovereign Identity principles. The trust layer incorporates machine learning models to continuously monitor node behavior and update trust scores in real time, enabling the system to respond to anomalies and malicious activities dynamically. To evaluate the practicality and effectiveness of the proposed framework, we developed a prototype system and conducted experimental validation in a simulated distributed environment. The results confirm that the integrated approach enhances authentication assurance, improves trust coordination, and supports sustainable scalability through efficient consensus and computation mechanisms. This work offers a promising direction for securing blockchain, IoT, and other decentralized systems. Future efforts will focus on field deployment, cross-domain interoperability, and regulatory compliance.
The widespread adoption of big data and AI technologies has accelerated the advancement of intelligent medical diagnostics. However, the sensitivity of medical data poses a dual challenge of privacy leakage and computational inefficiency in cross-institutional collaboration. Traditional federated learning (FL) schemes struggle to balance privacy protection, model accuracy, and communication costs, particularly for real-time processing of high-resolution medical images. To address this, we propose CZ-FLMed, a privacy-preserving FL framework integrating CKKS fully homomorphic encryption (FHE) and zkSNARKs zero-knowledge proofs. The framework employs a customized Convolutional Neural Network (CNN) for medical image training, the CKKS segmented encryption strategy for reducing communication overhead, and the lightweight Groth16 protocol for secure identity verification. This enables efficient encrypted model aggregation and authentication. The experiments results conducted in this paper on Chest X-Ray pneumonia dataset and MNIST handwritten digits demonstrate that CZ-FLMed achieves 83.05 % test accuracy in pneumonia classification. Compared to Paillier encryption, it reduces communication costs by 92.84 % and improves encryption efficiency by 404 times. Thus, the framework balances model accuracy, computational efficiency, and privacy preservation, offering a practical solution for multicenter medical collaboration.
For regional economies striving for both digital transformation and green development, existing blockchain consensus algorithms present a dilemma: Proof-of-Work (PoW) is notoriously energy-intensive, while Proof-of-Stake (PoS) and state-of-the-art high-throughput algorithms like Proof-of-Authority (PoA) fail to effectively incentivize the high-quality data contributions crucial for economic collaboration. To address this challenge, this paper proposes a novel Green Proof-of-Contribution (G-PoC) consensus algorithm. G-PoC introduces a multi-dimensional “Green Contribution” metric that quantifies a node's value to the network by holistically evaluating its data sharing quality, system stability, and energy efficiency. This mechanism incentivizes nodes to not only share valuable data but also to optimize their operational and energy footprints. The GPoC consensus process was designed, including a leader selection mechanism based on the contribution score. A simulation-based performance evaluation demonstrates that, compared to PoW, PoS and PoA, G-PoC significantly reduces energy consumption by up to 95% against PoW, while enhancing data-related transaction throughput and promoting decentralization. The results confirm that G-PoC provides a secure, efficient, and ecologically-sound technical solution for building collaborative data sharing platforms in regional economies.
Ruslan Kysil, István András Seres, Péter Kutas, Nándor Kelecsényi
This work explores the application and efficient deployment of (standardized) post-quantum (PQ) digital signature algorithms in the blockchain environment. Specifically, we implement and evaluate four PQ signatures in the Ethereum Virtual Machine: W-OTS+ , XMSS, SPHINCS+, and MAYO. We focus on optimizing the gas costs of the verification algorithms as that is the signature schemes’ only algorithm executed on-chain, thus incurring financial costs (transaction fees) for the users. Hence, the verification algorithm is the signature schemes’ main bottleneck for decentralized applications. We examine two methods to verify post-quantum digital signatures on-chain. Our practical performance evaluation shows that full on-chain verification is often prohibitively costly. Naysayer proofs (FC’24) allow a novel optimistic verification mode. We observe that the Naysayer verification mode is generally the cheapest, at the cost of additional trust assumptions. We release our implementation called poqeth as an open-source library.
Secure exchange of patient healthcare data is vital due to the rise of AI in the medical field. However, this advancement introduces challenges such as data breaches, privacy violations, and regulatory demands. Traditional centralized systems store all data in one location, increasing cyberattack risks. This study proposes a secure framework integrating Federated Learning, Blockchain, and Quantum Cryptography. Federated Learning enables decentralized model training without sharing raw data, preserving patient privacy. Blockchain ensures data integrity using an immutable distributed ledger. Quantum Key Distribution (QKD) and AES-256 encryption protect data during transmission and storage. Files are stored in the InterPlanetary File System (IPFS), and their unique Content Identifiers (CIDs) are recorded on the blockchain for tamper-proof verification. Only users with valid quantum-generated keys can decrypt and access the data, ensuring strong privacy and security.
S N Prajwalasimha, Nilesh Shelke, Dilip Kumar Jang Bahadur Saini, Amit Pimpalkar · 6 authors
Federated Learning (FL) is a decentralized collaborative AI training paradigm that maintains privacy of the data. FL is still susceptible to security attacks, malicious clients, and model integrity issues. To mitigate these issues, we introduce a Blockchain-Enabled Federated Learning (BFL) system that incorporates decentralized ledger technology to provide tamper-evident model aggregation, transparent client engagement, and verifiable updates. The suggested BFL framework uses smart contracts to enable automated trust management, zero-knowledge proofs (ZKPs) to facilitate privacy-enhanced authentication, and an incentive mechanism based on tokenized rewards to promote honest engagement. We also propose an adaptive consensus protocol that maximizes blockchain overhead while preserving high scalability for real-world applications like cybersecurity, healthcare, and Industrial IoT (IIoT). Experimental results on benchmark datasets show that BFL dramatically improves model robustness against data poisoning and adversarial attacks with a 15-25% improvement in attack resilience over state-of-the-art FL methods. Our work presents a complete blueprint for secure, privacy-preserving AI and establishes a foundation for the next generation of decentralized intelligence.