This paper proposes a novel distributed secure computation engine based on blockchain technology. The core claim is to leverage blockchain's inherent trust and traceability mechanisms to secure computation, guaranteeing the integrity and security of the resulting data. The proposed system employs zero-knowledge proofs and homomorphic encryption to facilitate secure computation while utilizing a blockchain to record the computation process and its outcome, thereby ensuring complete traceability. This represents a new approach to secure computation by directly integrating blockchain's capabilities, addressing limitations of traditional approaches and offering enhanced security and auditability. The system's architecture, core mechanisms, and potential applications are thoroughly detailed, highlighting its advantages and future directions.
This paper proposes a novel consensus mechanism for blockchain networks, termed Distributed Proof-of-Work with Differential Privacy Constraints (DPPoW). The core objective is to integrate differential privacy guarantees into a Proof-of-Work (PoW) system, mitigating the privacy vulnerabilities inherent in traditional blockchain designs. The proposed mechanism introduces calibrated noise directly into the hashing process, carefully controlled to achieve a specified ε-differential privacy level. Furthermore, it incorporates a distributed key generation scheme to bolster security and prevent centralized control. The design addresses the growing concern about data traceability and potential deanonymization of participants within blockchain networks. Mathematical formulations detail the noise injection process and the key generation protocol, demonstrating the feasibility and effectiveness of the approach. The primary innovation lies in the synergistic combination of PoW's security features with differential privacy, offering a robust solution for privacy-conscious blockchain applications. The system aims to balance security, anonymity, and computational efficiency, a challenging endeavor currently unmet by existing blockchain technologies.
This paper presents a novel zero-knowledge proof scheme constructed upon polynomial multi-variable rings. The core claim is to design a scheme that significantly enhances proof efficiency and security while addressing the computational complexity bottlenecks prevalent in existing approaches. The proposed mechanism leverages the unique properties of multi-variable polynomial rings to establish a streamlined proof and verification process, minimizing the risk of information leakage. Unlike traditional zero-knowledge proofs that often rely heavily on large number arithmetic, this scheme utilizes polynomial operations, leading to potentially improved performance. This work contributes to the field of cryptographic primitives by offering a new design paradigm rooted in algebraic structures, potentially unlocking avenues for more efficient and practical zero-knowledge proofs. The key contributions are a novel construction and a theoretical analysis demonstrating the security and efficiency gains. The scheme operates by encoding the statement to be proven as a polynomial equation in a multi-variable ring, and the prover generates a proof that allows the verifier to confirm the equation's validity without learning any information beyond the proof itself. This design aims to provide a more scalable and practical solution for zero-knowledge proof applications.
This paper presents a novel approach to zero-knowledge proof (ZKP) systems that dynamically generate and verify proofs in real-time, eliminating the need for pre-storage of complete proof data. The core mechanism leverages verifiable hash functions and verifiable computation circuits to enable dynamic proof generation and validation. This addresses the limitations of traditional ZKPs regarding large proof sizes and low generation efficiency, offering new security guarantees for large-scale distributed computations. The proposed system significantly reduces the storage requirements and computational overhead associated with ZKP systems, paving the way for more efficient and scalable cryptographic protocols. This work details the architecture, algorithms, and theoretical underpinnings of this dynamic ZKP system, highlighting its advantages and potential applications.
Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges regarding trust, security, and model accuracy. This paper presents a formal verification framework for FL systems, leveraging secure multi-party computation (SMPC) and formal verification techniques to rigorously analyze data flow and model updates. The framework aims to provide guarantees about privacy, security, and model accuracy, addressing the unique vulnerabilities present in FL architectures. We define a mathematical model of an FL system, incorporating key elements such as clients, servers, and communication protocols. This model is then subjected to formal verification, utilizing techniques like model checking and symbolic execution to identify potential security breaches and inaccuracies. The results demonstrate the feasibility and effectiveness of applying formal verification to FL, offering a robust approach to ensuring the trustworthiness of these systems. Key performance metrics, including privacy loss, communication overhead, and model accuracy deviations, are quantified and analyzed within the verification process. The framework contributes to the development of more reliable and secure FL applications, particularly in sensitive domains such as healthcare and finance.
This paper presents a novel approach to achieving both strong differential privacy guarantees and Byzantine fault tolerance (BFT) in distributed systems. The core of the system leverages a verifiable random function (VRF) based consensus protocol built upon a Byzantine fault-tolerant distributed ledger. This architecture effectively addresses the inherent challenges of protecting sensitive data while maintaining system availability and data integrity even in the presence of malicious actors. The system utilizes VRF to mask computation results, providing differential privacy, and the distributed ledger to ensure robustness against Byzantine attacks. The key innovation lies in the verifiable and layered design, offering a rigorous framework for combining these two traditionally disparate goals. We demonstrate a theoretical framework for the system's operation, outlining the protocols involved and the mathematical properties utilized to guarantee both privacy and fault tolerance. The system achieves a privacy parameter ε and a fault tolerance threshold τ, where ε controls the privacy loss and τ represents the level of Byzantine fault tolerance. The system's architecture is designed to be adaptable to various distributed applications, offering a robust solution for sensitive data processing in challenging environments.
This paper proposes a novel blockchain consensus mechanism termed "Distributed Proof-of-Work with Quantum Key Distribution" (DPW-QKD). The core idea is to leverage quantum key distribution (QKD) to replace computationally intensive hash functions in traditional Proof-of-Work (PoW) systems, thereby significantly reducing energy consumption. The system operates by nodes generating cryptographic keys through QKD, which are then utilized in a distributed QKD protocol to verify transaction validity. This approach eliminates the need for miners to solve complex cryptographic puzzles, creating a more energy-efficient and potentially more secure consensus model. The paper outlines the architecture, key components, and operational principles of the DPW-QKD system, highlighting its advantages and potential challenges. Mathematical formulations are presented to illustrate the key processes and security considerations within the system. The research aims to explore a viable pathway towards a sustainable and robust blockchain technology, driven by the inherent security of quantum mechanics.
Open access
2 source records
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Blockchain technology relies heavily on consensus protocols to ensure data integrity and security. However, the decentralized and often complex nature of these protocols makes formal analysis and design challenging. This paper proposes a novel approach to formally specifying and analyzing blockchain consensus protocols using game theory. We model the consensus process as a strategic game, considering the incentives of different participants and deriving the resulting equilibria. This framework allows for a rigorous assessment of protocol design, identifying vulnerabilities and potentially optimizing performance. The core claim is that game theory provides a viable tool for both designing and analyzing blockchain consensus protocols. We explore various consensus mechanisms, including Proof-of-Work and Proof-of-Stake, demonstrating the application of our method. The key contribution is a theoretical framework offering a systematic approach to blockchain consensus design, moving beyond intuitive assumptions and enabling a more robust and secure system.
This paper proposes a novel approach to distributed trusted computation leveraging the inherent properties of blockchain technology. The core claim is that blockchain's consensus mechanisms and data integrity guarantees can facilitate a secure and trustworthy distributed computing environment, effectively addressing the trust issues prevalent in cloud computing. The proposed mechanism involves decomposing computational tasks into smaller sub-tasks, which are then collaboratively executed by nodes within a blockchain network. Smart contracts are employed to manage task scheduling and validate the results. This system offers an alternative to traditional trust models, utilizing cryptographic techniques and distributed consensus for enhanced security and transparency. The research explores the potential of blockchain to fundamentally transform the landscape of distributed computing, providing a robust solution for sensitive computations and data processing. The paper focuses on the technical design and theoretical underpinnings of this approach, outlining key components and potential challenges.
This paper proposes a novel approach to blockchain consensus mechanisms by leveraging the principles of Distributed Proof Theory (DPT). DPT, traditionally applied to the analysis of distributed systems and formal verification, offers a rigorous mathematical framework for reasoning about logical consistency and correctness. We argue that mapping existing blockchain consensus protocols—such as Proof-of-Work, Proof-of-Stake, and Byzantine Fault Tolerance—onto the formal language of DPT allows for a deeper understanding of their vulnerabilities and facilitates the design of more secure and efficient algorithms. The core mechanism involves identifying and eliminating logical fallacies inherent in the consensus process, ultimately leading to a more robust and mathematically grounded design. This work presents a theoretical framework and outlines a methodology for applying DPT to blockchain, potentially leading to significant advancements in blockchain security, scalability, and overall reliability. The key contribution lies in the application of a sophisticated abstract mathematical theory to a practical problem within the blockchain domain, offering a unique perspective on the challenges inherent in decentralized consensus.
Authentication in Web 3.0 faces a structural conflict. Systems that offer full anonymity leave no lawful way to identify a malicious actor. Systems built for accountability expose a persistent wallet address to blockchain-graph analysis, or fall back on centralized key recovery. Existing designs solve one side of this conflict at the cost of the other. This paper presents PEUAP-W3, a Privacy-Enhanced and User-centric Authentication Protocol. Its contribution is the integration of five established components into a single deployed and formally analyzed system. A Circom 2 circuit of 1579 Groth16 constraints proves four facts in a single 192-byte on-chain proof: knowledge of an opening of the session credential commitment, an SpO2 value inside an 85–100% band, single-use nonce binding, and HMAC integrity. Shamir (k = 2, n = 3) sharing distributes the identity payload across three independent relays. The coordinator reconstructs an identity only after a threshold vote has been recorded on chain. Credentials are issued as W3C Verifiable Credentials 2.0 in did:key form. Four Solidity contracts verify the proof on Ethereum Sepolia. Verification costs about 241,000 gas and takes roughly 3 ms. ProVerif and Scyther find no attack under the Dolev–Yao model. A concurrency sweep to 500 simultaneous requests completes 1191 requests with zero failures at about 15.4 requests per second. A behavioral gate screens commodity abuse as a supplementary control; it is not treated as a security boundary. Against a nine-property framework, PEUAP-W3 satisfies six properties. Three remain conditional and are not verified in the current deployment: biological-origin assurance and digital replay prevention, both of which need an attested sensor; and GDPR erasure equivalence. Here, formally verified refers to the protocol models and theorems, not to the complete deployed software.
Background: In recent years, the Internet of Medical Things (IoMT) has transformed the healthcare sector through real-time patient monitoring and continuous data collection.However, transmitting sensitive medical information over public networks exposes IoMT systems to significant security threats, while emerging quantum computing technologies challenge the reliability of traditional cryptographic systems.Objective: The objective of this study is to propose PQAC-BIoMT, a secure and robust model for remote user authentication and access control in IoMT environments, capable of withstanding both conventional and quantum attacks.Methods: This article proposes a decentralized authentication framework that integrates post-quantum cryptography using Kyber Public-Key Encryption (Kyber-PKE) into blockchain-based smart contracts.Fog computing nodes are used to reduce the authentication latency and improve the system scalability.A role-based authorization mechanism is integrated to link user identities to functional roles and enforce authorization to medical data and system resource access.Formal security verification is conducted using Burrows-Abadi-Needham (BAN) logic to validate the correctness of authentication, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is used to assess resistance to known attacks.PQAC-BIoMT is further evaluated through a comparative analysis of the computational load, energy consumption and security properties.Results: Our security analysis demonstrates that PQAC-BIoMT effectively resists common attacks while providing quantum-resistant protection against them.The performance evaluation shows that the proposed scheme achieves relatively lower computational and energy overhead compared to existing approaches, making it suitable for resource-constrained IoMT devices.Conclusion: The proposed PQAC-BIoMT scheme delivers a secure, quantum-resilient authentication and authorization mechanism for IoMT systems, enhancing both data protection and operational efficiency, which can support practical deployment in real-world IoMT applications.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Federated learning (FL) has emerged as an important distributed machine learning paradigm allowing many users to train a model together without sharing raw data. However, FL’s decentralized design makes it very susceptible to both Byzantine and coordinated model poisoning attacks, where a few malicious rounds of gradients are strategically inserted to reduce the overall integrity of the model. Even the existing Byzantine-resilient aggregation methods such as geometric median, Krum, and trimmed mean are vulnerable to sophisticated, coordinated poisoning attacks that leverage statistical gaps in outlier detection. In this article, we present FedSentinel, a novel Byzantine-resilient federated learning framework that combines cryptographic gradient attestation with adaptive trust-weighted aggregation to protect against coordinated model-poisoning attacks, which are among the most serious challenges. Three key innovations are introduced in FedSentinel Cryptographic Gradient Attestation Protocol (CGAP) that utilizes commitment schemes and zero-knowledge range proofs to ensure the integrity of the gradients and verify that submitted updates satisfy predefined norm and direction constraints; in the current protocol configuration, CGAP provides gradient integrity verification rather than full gradient confidentiality from the server Dynamic Trust-Weighted Robust Aggregation (DT-RoA): the computation and updating of per-client trust scores based on historical gradient consistency and cross-validation signals; and Coordinated Attack Detection Engine (CADE): based on spectral analysis of the gradient covariance matrices, it detects and isolates colluding Byzantine clients. By participating in up to 100 clients on CIFAR-10, CIFAR-100, FEMNIST, and Sentiment140 datasets, extensive experiments conducted under up to 30% Byzantine adversaries reveal that FedSentinel achieves 91.36% average global accuracy, whereas state-of-the-art defenses such as FLTrust, FLAME, RoFL, ShieldFL, and DnC achieve 83.83–86.94%. FedSentinel outperforms these defenses by 4.42–7.53% in terms of accuracy, while decreasing attack success rates by 53.2% under coordinated Byzantine backdoor attacks. The proposed framework offers a promising approach for federated learning that is verifiable and trustworthy in adversarial environments.
The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
With the rapid evolution of the Decentralized Finance (DeFi) ecosystem, stablecoins have emerged as a critical infrastructure bridging the cryptocurrency market with traditional financial paradigms. However, stablecoin systems rely heavily on smart contracts to execute automated operations. The immutable nature of these systems post-deployment means that the exploitation of security vulnerabilities can lead to irreversible, massive economic losses and potentially trigger systemic financial risks. Current research on stablecoin smart contract security faces challenges such as a lack of domain-specific targeting and the obsolescence of static defense models. To address this, this paper systematically analyzes common attack vectors in stablecoin environments and proposes a practical, real-time dynamic defense architecture. By analyzing 12 real-world security incidents, we elucidate the underlying mechanisms of high-risk patterns such as reentrancy attacks, oracle manipulation, and composite flash loan attacks. Concurrently, we construct a real-time anomaly detection model utilizing multi-dimensional on-chain temporal features and the Bi-LSTM algorithm. Experimental results demonstrate that this model achieves a classification accuracy of 96.61\%, with an average recall rate of 97.70\% for malicious attack samples, and a single inference latency ranging from 1.5 to 2.8 milliseconds.
The classical taxonomy of remote voting security requirements is organized into two levels: basic and counter-attack requirements. This classification has remained largely unchanged over a decade, even through scheme innovations such as verifiable re-voting, tally-hiding, and post-quantum protocols. However, this stability does not survive the new threat model of adversarial artificial intelligence (AI). A security requirement is considered promoted when AI raises the adversarial grade at which it must be defended above its original assumption. In this paper, we introduce requirement promotion as a framework for re-evaluating the classical taxonomy, contending that the destabilizing factor is not new cryptography but the emergence of a new adversary. We analyze five requirements under both AI-amplified threats and AI-enabled defenses, demonstrating that promotion fires selectively. Basic requirements such as privacy, fairness, and eligibility undergo tier promotion to counter-attack grade, driven by machine-learning-based deanonymization, pre-tally outcome inference, and synthetic-identity fraud. Incoercibility undergoes supra-tier promotion, surpassing the existing counter-attack toolkit, as deepfake-generated coercion evidence compromises the fake-credential assumptions of classical coercion-resistant schemes. Verifiability, where AI-based defense is robust, resists headline promotion but acquires a new sub-requirement at its seam with software independence: verifying the opaque machine-learning components in the audit pipeline itself. We then propose a reference architecture, integrating existing primitives such as lattice-based zero-knowledge proofs, deniable re-voting, statistical election forensics, and time-lock decryption into a layered design that addresses the promoted requirements, with explicit analysis of residual gaps.
Banik and Kumar (Journal of Information Security and Applications 2023) proposed a blockchain-based public key encryption with keyword search for medical data sharing in cloud environments. In this comment paper, we show that the Banik-Kumar scheme leaks keyword information from both ciphertexts and trapdoors.
Abstract In the era of artificial intelligence, the AI-generated image (AIGI) market is an emerging sector that faces significant challenges related to ownership, privacy, and security. These issues, especially prevalent in NFT markets, can be effectively addressed by the integration of advanced technologies such as blockchain, the InterPlanetary File System (IPFS), and Quantum Key Distribution (QKD). This study proposes a comprehensive trading framework that incorporates state-of-the-art methodologies and algorithms to simulate the entire AI image trading process. For image generation, the framework utilises diffusion models (LCM-LoRA + SDXL) and Generative Adversarial Networks (GANs), employing LCM-LoRA and LCMScheduler from Stable Diffusion XL Base 1.0 to accelerate image generation and reduce inference steps. Implementation is carried out using PyTorch and the Diffusers library, running on a CUDA-enabled GPU. The generated images are securely stored in the distributed IPFS storage system, while decentralised trading is facilitated through integration with the Hyperledger MiniFab tool. The framework supports multiple trading mechanisms, including Blind English Sealed-Bid Auctions (BESEA), fixed-price auctions with a first-come, first-served (FCFS) model, Dutch auctions with royalty redistribution, and fractionalized auction trading. To ensure secure communication between buyers and sellers, the BB84 QKD protocol is employed to generate a shared secret key with information-theoretic security. The generated key is processed through key sifting to derive a symmetric key, which is zero-padded to the 256-bit length required by AES-256-CBC. It is then directly used as the encryption key to encrypt AI-generated images and their associated metadata before storage on IPFS. Experimental evaluation across four auction mechanisms and up to 250 NFTs shows the Dutch auction achieving the highest sales volume (up to 211 NFTs sold) and the Fractionalized auction the highest revenue ( 98,270). The IPFS storage maintains sub-0.75-second upload latency with 100% file verification success. Blockchain-layer benchmarking across 100–500 participants records mean chaincode latency ranging from approximately 22 to 53 seconds and throughput of 0.018–0.045 TPS.
Quantum computers pose a fundamental threat to blockchain systems that rely on elliptic-curve cryptography. This work reviews the quantum vulnerabilities and associated economic risks of major blockchain platforms, with a focus on Bitcoin, Ethereum, and Solana. We distinguish between at-rest, on-spend, and on-setup attacks and identify the blockchain components most exposed to quantum adversaries. We further review practical migration strategies toward post-quantum security, including NIST-standardized digital signatures and emerging solutions for Solana, Algorand, and Ethereum.
Homomorphic-encryption blockchain frameworks for IoT sensor aggregation generally rely on classical cryptographic hardness assumptions and seldom account for network topology in liveness and performance analysis. This work introduces Phi-PHE-BC, a topology-aware homomorphic blockchain architecture for secure and privacy-preserving IoT sensor data aggregation. The framework combines threshold Paillier decryption with graph-parameterized security and performance analysis, linking protocol behavior to the validator graph. On-chain Paillier ciphertexts support homomorphic aggregation while providing IND-CPA confidentiality under the Decisional Composite Residuosity assumption, and authentication signatures provide EUF-CMA transaction integrity. Threshold partial-decryption shares are protected by a noise-flooding wrapper that provides information-theoretic privacy under the configured statistical-hiding condition. Under partial synchrony and Byzantine fault-tolerance assumptions, liveness requires validator connectivity kappa(Gv) >= f+1. We derive topology-dependent throughput bounds for tree, star, mesh, and scale-free networks, together with a per-block communication-cost model. A game-theoretic analysis shows that honest validator participation is a dominant strategy under the stated utility model, yielding an all-honest Nash equilibrium. Experiments on Hyperledger Fabric 2.5 show lower end-to-end latency than the selected traditional PHE-blockchain baseline while maintaining controllable threshold-decryption overhead. Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system.
This research presents a novel decentralized identity management system leveraging Ethereum smart contracts and cryptographic protocols to enable self-sovereign digital identities. Traditional centralized identity solutions pose risks related to data privacy, security, and user autonomy. To address these limitations, we propose a blockchain-based architecture integrating smart contracts for identity governance, IPFS for decentralized attribute storage, and threshold cryptography for private data recovery. Our dual-contract model (Identity Contract and Recovery Contract) facilitates secure identity creation, verifiable attribute attestation, and robust key recovery through social consensus. We introduce a privacy-preserving mechanism using encryption-key splitting among trusted peers to enable recovery of encrypted off-chain data. Implementation uses Web3.js, Solidity, and QR-code-based communication, abstracting cryptographic complexities from end-users. Security analysis addresses replay, man-in-the-middle, Sybil, and multi-user compromise attacks with mitigations including challenge-response authentication and time-delayed contract execution. This work contributes a cryptographically secure, user-centric identity framework ensuring data sovereignty, recoverability, and interoperability within the Ethereum ecosystem.
PQ-Sortition is a post-quantum cryptographic sortition protocol constructed from the NTRU lattice hardness assumption and instantiated using Falcon-512 (FN-DSA). The construction uses deterministic Falcon signing to obtain a reproducible, publicly verifiable proof and combines it with a consensus-layer commit-then-reveal mechanism to address the lack of unconditional uniqueness inherent in GPV-style lattice signatures. The work introduces NTRU-Sortition, a many-time lattice-based verifiable random function construction, and provides formal analyses of third-party uniqueness, pseudorandomness under the NTRU-SIS assumption in the Random Oracle Model, and provability. The paper further defines PQ-Sortition as a post-quantum proof-of-stake leader-election protocol using a historical randomness beacon, stake-weighted sortition, adaptive difficulty, equivocation slashing, and grinding resistance. The Falcon-512 instantiation provides a 32-byte output and proofs of up to 666 bytes. The paper also presents concrete performance measurements, security parameters, consensus integration details, comparisons with prior post-quantum VRF constructions, and open research problems.