This paper introduces ZKAP (Zero-Knowledge Audit Protocol), a cryptographic protocol in which a machine-learning inference is coupled to a zero-knowledge proof of constraint satisfaction in such a way that the output of the inference cannot be released on any observable channel unless the proof verifies. The protocol rests on two principal technical contributions: The Certified Stack — a composite object that cryptographically binds the model weights, a mandatory bit-integrity policy (integer quantisation), the inference runtime stack and the hardware configuration under a single commitment (RootHash), which is included as a public input to every proof produced by the system. Prove-before-output enforcement — a family of four embodiments (hardware output gate, trusted-execution-environment release path, silicon-level embodiment, syscall-intercepting software runtime) in which the output of the inference is physically blocked from leaving the system until the accompanying proof has verified against the Certified Stack commitment and against a constraint set signed by an external authority. Three supporting mechanisms are described: authority-signed formal constraints with a five-type taxonomy, a per-inference hash chain with external anchoring, and a three-party cryptographic separation of duties. The paper further discusses practical relaxations of the strict release semantics for real-time workloads. A soundness theorem is stated against a polynomial-time adversary controlling the operator of the inference, and regulatory implications for high-risk AI systems under Regulation (EU) 2024/1689 (Artificial Intelligence Act) are discussed. Priority declaration. The inventive mechanisms described in this paper are the subject of Bulgarian patent applications BG/P/2026/114317 (filed 30 March 2026) and PTBG202600000316742 (filed 12 April 2026). This preprint establishes the academic priority of the author, as of the date of the deposit timestamp, over the specific technical constructions described in Sections 3 and 4 of the paper. Access note. This deposit is under embargo until 31 March 2027. During the embargo, metadata (title, abstract, author, keywords, references) are publicly visible; the full text is not publicly accessible. The embargo date coincides with the Paris Convention priority deadline of the underlying Bulgarian patent application BG/P/2026/114317. The DOI assigned at deposit time serves as a timestamp for academic priority purposes, independent of the file's accessibility.
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In client-server applications such as copyright protection and content moderation, learning-based perceptual hashing compresses images into compact binary codes whose Hamming distances approximate perceptual similarity. Clients then transmit these codes to servers for comparison. However, this approach faces dual challenges: algorithmically, how to effectively balance robustness and discriminability while mitigating bit imbalance issues; protocol-wise, transmitting these hashes compromises client privacy through content inference and cross-platform user tracking. To address these challenges, we propose a trustworthy privacy-preserving framework that integrates deep hashing with zero-knowledge proofs. The framework comprises: (1) A robust deep hashing module that generates discriminative binary codes by optimizing a composite objective function composed of the Angular Triplet and quantization losses, while using a multi-scale strategy to correct bit imbalance. (2) A privacy-preserving similarity comparison protocol based on Sumcheck and Logarithmic Lookup, which enables clients to locally prove batch Hamming distance relationships against public dataset entries without disclosing their hash values. We conducted comprehensive evaluations to demonstrate the practicality and efficiency of our design compared to existing schemes. Source code is available at https://github.com/mengdehong/zkph.
Advanced Steganography and Watermarking Techniques
Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load.
With the rapid development of Generative Artificial Intelligence (GAI), large-scale AI-Generated Content (AIGC) has been widely produced, raising critical challenges in trustworthy copyright management. Blockchain-based copyright registration or trading have become a research hotspot, but existing solutions focus on isolated stages and fail to support the full lifecycle of AIGC content, while copyright management performance, infringement detection capability, and copyright query efficiency remain challenging. To address these challenges, we designed a dual-layer blockchain framework for full-lifecycle AIGC copy-right management, which supports coordinated copyright registration, verification, trading, and traceability. The proposed framework adopts a dual-layer architecture with a main chain and multiple sub-chains, and integrates sharding with a Directed Acyclic Graph (DAG) parallel ledger to improve system scalability. Specifically, a Perceptual Hash (pHash)-based similarity detection method is introduced for copyright registration to identify plagiarism and unauthorized duplication; a hybrid indexed sharded query mechanism is designed for efficient and verifiable copyright verification; and cryptographic techniques together with zero-knowledge proofs are incorporated to enable secure and non-repudiable copyright trading. Experimental results show that the designed framework delivers about 1.1× higher throughput and achieves roughly a 29× reduction in transaction latency compared with single-chain blockchains, while the proposed query mechanism reduces query latency by up to 56× across different shard scales. These results validate the capability of the proposed framework to support secure, efficient, and scalable AIGC copyright management.
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
The transition from isolated distributed ledgers to a unified “Internet of Value” is hindered by the lack of efficient, verifiable, and privacy-preserving cross-chain data retrieval mechanisms. While asset bridging has matured, generalized data indexing remains a critical bottleneck, constrained by the semantic gap between heterogeneous storage layouts and the prohibitive verification tax of cryptographic proofs. In this paper, we present HyperCross, a novel semantic-aware zero-knowledge indexing framework designed to bridge this divide. We first formalize the heterogeneous cross-chain storage optimization problem (HCCSOP) and prove its NP-completeness. To tackle this, HyperCross employs a synergistic tri-layered architecture. At the semantic layer, we introduce a unified data abstraction (UDA) that leverages category-theoretic functors and schema morphisms to ensure mathematically rigorous state mapping for both simple assets and complex smart contract logic. At the indexing layer, a zero-knowledge learning index (ZKLI) shifts prediction intelligence to the client side, integrating zk-SNARKs with silent oblivious transfer to achieve constant-time verification (O(1)) while concealing access patterns. Finally, a multi-level cache (MLC) utilizes predictive prefetching with Δ-bounded staleness to mask network latency. Extensive evaluations demonstrate that HyperCross reduces query latency by 2.4× and storage overhead by 40% compared to state-of-the-art baselines, establishing a scalable foundation for data-intensive inter-chain applications.
Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated model commercialization to reinforce the model performance, including licensing or trading Deep Neural Network (DNN) models. However, DNN model trading may violate the benefit of the model owner due to unauthorized replications or misuse of the model. Model identity auditing is a challenging issue in protecting DNN model ownership, and verifying the integrity and ownership of models is one of the critical obstacles. In this paper, we focus on the above issue and propose anAccumulator-enabledAuditing forDecentralizedIdentity of DNNModel (A2-DIDM) that utilizes blockchain and zero-knowledge techniques to protect data and function privacy while ensuring the lightweight on-chain ownership verification. The proposed model presents a scheme of identity records via configuring model weight checkpoints with zero-knowledge proofs, which incorporates predicates to capture incremental state changes in model weight checkpoints. Our scheme ensures both computational integrity and programmability in DNN training process so that the uniqueness of the weight checkpoint sequence in a DNN model is preserved. A2-DIDM also addresses privacy protections in decentralized identity. We systematically analyze the security and robustness of our proposed model and further evaluate the effectiveness and usability of auditing DNN model identities. The code is available athttps://github.com/xtx123456/A2-DIDM.git.
Miksi is a decentralized blockchain protocol that uses zero knowledge proofs (ZKP) and cryptographic commitments to enable anonymous transactions within a secure decentralized network. The system consists of three components: the main application (miksi.ts), the ZKP circuits (deposit and withdraw.circom), and the smart contracts (Miksi.sol, deposit-verifier.sol, and withdraw-verification.sol). Each component plays a pivotal role in the overall security of the system, and we evaluate them for common vulnerabilities such as improper input validation, reentry attacks, and weaknesses in proof verification. Through this analysis, we highlight both the strengths and potential areas for improvement in ensuring secure, anonymous cryptocurrency transactions.Keywords: Ethereum, ZKP, blockchain, DeFi, anonymity, mixer
Zero-knowledge proof (ZKP) provers remain costly because multi-scalar multiplication (MSM) and number-theoretic transforms (NTTs) dominate runtime as they need significant computation. AI ASICs such as TPUs provide massive matrix throughput and SotA energy efficiency. We present MORPH, the first framework that reformulates ZKP kernels to match AI-ASIC execution. We introduce Big-T complexity, a hardware-aware complexity model that exposes heterogeneous bottlenecks and layout-transformation costs ignored by Big-O. Guided by this analysis, (1) at arithmetic level, MORPH develops an MXU-centric extended-RNS lazy reduction that converts high-precision modular arithmetic into dense low-precision GEMMs, eliminating all carry chains, and (2) at dataflow level, MORPH constructs a unified-sharding layout-stationary TPU Pippenger MSM and optimized 3/5-step NTT that avoid on-TPU shuffles to minimize costly memory reorganization. Implemented in JAX, MORPH enables TPUv6e8 to achieve up-to 10x higher throughput on NTT and comparable throughput on MSM than GZKP. Our code: https://github.com/EfficientPPML/MORPH.
DilithiumBar Architecture DilithiumBar is a high-performance, post-quantum (PQ) blockchain protocol engineered to withstand the cryptographic threats posed by future quantum computing. By replacing standard ECDSA signatures with the CRYSTALS-Dilithium5 lattice-based scheme, the system ensures long-term security without compromising operational efficiency. The protocol integrates a sophisticated suite of modern blockchain innovations: Post-Quantum Security: Native implementation of NIST-standardized lattice cryptography to secure transactions and state transitions. High-Finality Consensus: A Byzantine Fault Tolerant (BFT) mechanism that provides deterministic 10-second finality, significantly faster than traditional Proof-of-Work systems. Advanced State Management: Utilizes a Merkle-Patricia Trie for verifiable state tracking and atomic database operations to ensure data integrity. Dynamic Economics: An EIP-1559 inspired fee market that stabilizes transaction costs and optimizes network throughput. While the protocol acknowledges the increased bandwidth and storage requirements typical of post-quantum primitives, it balances these trade-offs through an optimized architecture designed for the next era of decentralized finance.
The Distributed Medical Report Management System using Blockchain Technology is designed to revolutionize the management and sharing of medical reports by integrating blockchain technology, specifically targeting data security and patient privacy.This system enhances the protection of Electronic Health Records (EHR) through sophisticated cryptographic methods and decentralized storage solutions.It consists of four core modules-Hospital, Doctor, Patient, and Receptionist-each tailored to manage specific healthcare tasks.The Hospital Module enables secure management of patient records and administrative tasks, creating a transparent and unchangeable ledger that boosts data integrity and builds trust.The Doctor Mod-ule allows healthcare professionals to securely access and update patient records, with cryptographic safeguards ensuring that only authorized users can see or alter sensitive information, significantly reducing the risk of data breaches.The Patient Module empowers individuals by providing direct access to their medical records, allowing them to monitor their health history and control who can view their data.The Receptionist Module streamlines administrative functions, such as appointment scheduling, while securely logging all interactions on the blockchain.By replacing traditional paper-based systems, this blockchain-centric approach offers a more secure, efficient, and user-friendly method for managing sensitive patient data.Advatages include enhanced security through blockchain protection, decentralized storage minimizing risks of data loss, improved accessibility for authorized healthcare providers, and increased patient control over their health information.Ultimately, the Distributed Medical Report Management System demonstrates the transforma-tive potential of blockchain technology in creating a secure framework for managing patient records, addressing challenges in healthcare data management, and paving the way for a more efficient healthcare system.
This repository/dataset presents the Matrix Cryptographic Tree (MCT), a novel cryptographic primitive based on the special linear group SL(2, \mathbb{Z}). Unlike traditional hash-based Merkle Trees, MCT is fully reversible and structure-preserving, allowing for unique algebraic verification without information loss. The core of this research is the integration of Unified Zero-Knowledge Proofs (ZKP). MCT enables advanced privacy-preserving operations, such as proving data similarity (L1 norm distance), prefix/suffix matching, and existence proofs, all without revealing the underlying data.
Rabib Jahin Ibn Momin, Ahmed Mahir Sultan Rumi, Rezwana Reaz
Academic examination systems worldwide continue to rely on centralised, opaque record-keeping that is often vulnerable to credential forgery, result tampering, examiner bias, and the absence of transparent re-evaluation pathways. Existing blockchain-based approaches in education focus predominantly on post-hoc certificate storage or online-only examination portals, leaving the complete onsite examination lifecycle, from conducting exams through scrutiny, largely unaddressed. This paper proposes ParikkhaChain, a blockchain-based framework that covers the entire examination lifecycle of an onsite examination system with three distinguishing contributions: (i) anonymous script evaluation through cryptographic hashing of answer scripts before examiner access, thereby eliminating identity-based bias; (ii) a transparent evaluation and scrutiny workflow backed by an immutable on-chain audit trail that records every mark submission and grade revision; and (iii) inclusion of privacy-preserving verification using zero-knowledge proofs and off-chain storage mechanisms. The system is architected around four Solidity smart contracts deployed on the Ethereum blockchain. The proposed architecture is the first initiative to our knowledge to support physical examination process, anonymous marking, and re-evaluation transparency. We successfully simulate full exam cycles of an onsite exam to grade-sheet generation using a working prototype on a large scale of 100 courses and hundreds of teachers and students. The experimental results show that the system can manage online examinations of hundreds of courses, students and faculties efficiently with great throughput, low storage, and transaction cost. Our codebase is available in open source form at https://github.com/AhmedRumi/CSE6608-ParikkhaChain
Blockchain-Driven Healthcare Platform with Access-Controlled Record Management is a decentralized application designed to enhance the security, privacy, and accessibility of medical records. Traditional healthcare systems rely on centralized storage, making sensitive patient data vulnerable to breaches, manipulation, and unauthorized access. This project utilizes blockchain technology to provide a secure and tamper-proof environment for storing and managing healthcare data. Smart contracts are implemented to enforce access control, allowing patients to grant or revoke permission to doctors and healthcare providers. Medical records are securely stored using decentralized storage mechanisms, while blockchain maintains immutable references to ensure data integrity. The platform integrates Web3 technologies for secure user authentication and seamless interaction with the blockchain network. By eliminating intermediaries, the system improves transparency and trust among stakeholders. This solution demonstrates an efficient approach to managing healthcare data, ensuring confidentiality, integrity, and availability while addressing the limitations of traditional healthcare record systems in a modern, digital environment.
ABSTRACT E-voting in polarized contexts requires a strict balance between public verifiability, ballot secrecy, andcoercion resistance. Traditional centralized systems lack transparency, while fully decentralized modelsface scalability and privacy issues. This paper proposes a hybrid architecture compliant withOSCE/ODIHR standards [1] for low-trust environments. The protocol decouples identity from voting anoff-chain Oracle manages authorization via cryptographic tokens, while the Waves DLT acts as animmutable bulletinboard.Utilizinghomomorphicencryption[2],Zero-KnowledgeRangeProofs(ZKRP) [3],and Distributed Key Generation (DKG) [4], the system ensures End-to-End Verifiability (E2E) bydelegating tallying to auditable scripts. Finally, the study examines model limitations, specificallyregarding endpoint vulnerabilities and physical constraints on coercion resistance. KEYWORDS E-Voting, Distributed Ledger Technology, Homomorphic Encryption, End-to-End Verifiability, ZeroKnowledge Proofs PDF LINK: https://ijcionline.com/paper/15/15226ijci01.pdf VOLUME LINK: https://airccse.org/journal/ijci/Current2026.html MORE DETAILS: https://airccse.org/journal/ijci/index.html
We present DSKAG-IT-SIG, a family of post-quantum transaction signature schemes that achieve computational existential unforgeability under adaptive chosen-message attack, built on the DSKAG deterministic key-derivation layer. The construction derives per-transaction MAC keys through DSKAG, a deterministic symmetric key agreement protocol requiring no key transmission, no handshake, and no public key infrastructure. We prove (Theorem 1) that for an adversary making q adaptive chosen-message queries, existential forgery advantage in standard mode is at most q * 2^{-128} plus the PRF distinguishing advantage of HMAC-SHA256, reducing to the pseudorandomness of DSKAG-derived keys and the PRF security of HMAC-SHA256 under a uniform key; the ideal-cipher-model analysis gives the same q * 2^{-128} bound in idealized form. We prove (Theorem 2) that cross-domain forgery advantage is at most 2^{-128} + epsilon_iso, reducing to the key-separation properties of DSKAG across policy domains. The construction is computationally secure and is not unconditionally secure. DSKAG key derivation is built on HKDF-SHA512 (RFC 5869) over HMAC and SHA-512, and the shared base is established once via FIPS 203 ML-KEM, so security reduces throughout to standard FIPS-based symmetric and hash primitives. The scheme's post-quantum security rests on symmetric and hash hardness for authentication and on lattice hardness for the one-time base alone: the construction presents no integer-factorization or discrete-logarithm structure, so Shor's algorithm has no target and does not apply, and the operative quantum attack is Grover search, which yields at most a quadratic speedup against the 256-bit HMAC-SHA256, SHA-2, and SHA-3 primitives and preserves a 128-bit quantum security level. Because buffer uniqueness derives from tx_seq monotonicity rather than hash collision resistance, the security argument does not depend on the collision property, the hash property most weakened by quantum search. Standard-mode signatures are 30 bytes, a 97.8% reduction versus Falcon-512 (666 bytes) and compatible with ISO 20022 SWIFT message fields without re-engineering. The NexusKey composite policy digest binds asset class, jurisdiction, KYC level, and chain identity into the key derivation path; policy bypass is cryptographically equivalent to key forgery. A four-layer UltraHonk zero-knowledge proof system (143,802 gates, no trusted setup, 16 KB proof) verifies policy compliance wherever policy is enforced, off-chain in governance, cloud, and payment-processing deployments, and, where permissionless public auditability is required, on-chain; the on-chain Solidity verifier is deployed on Ethereum Sepolia and Arbitrum Sepolia. Version 2.3. 18 pages, 8 tables. Changes from v2.2: concrete finite bounds replacing generic negl(lambda) in Properties 1 and 2; buffer uniqueness derived from tx_seq monotonicity (no SHA3 collision resistance dependency); explicit ideal cipher model and standard model dual framing for HMAC analysis; formal separation of empirical and theoretical claims.
Open access
2 source records
Cryptography and Data Security
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Riya Sapra, Shal, Ishika Kataria, Sarita · 5 authors
A distributed ledgers information base split throughout the computer group's centres. As a data set, a blockchain stores information electronically in an elevated structure. Blockchain technologies are well-known for their role in cryptographic currency systems, such as Bitcoin, in maintaining a secure and decentralised record of transactions. The benefit of a blockchain is that it ensures the privacy and authenticity of a records while also establishing confidence in the absence of a trusted third party. The suggested system is a blockchain-based decentralised organ transplant app. Patients would use a web application to submit their information, which include their healthcare ID, blood group, tissue type, and state. Until a patient is in serious situation, the program would operate on a first-in, first-out basis.
Dr. P. C. Prabhu Kumar, P. Poojitha, K. Satheesh Kumar, M. Charan Kumar · 6 authors
The rapid digital transformation of the healthcare and drug sector has increased the reliance on cloud infrastructures for storing and exchanging Electronic Health Records (EHRs), raising significant concerns regarding privacy breaches, unauthorized access, and data integrity. To overcome these challenges, this project proposes a secure, patient-centric medical and drug data-sharing framework that integrates blockchain technology with distributed cloud storage. In this system, patients upload encrypted Personal Health Records (PHRs) to an untrusted cloud server while maintaining complete control over access permissions. A semi-trusted Setup and Re-Encryption Server (SRS) manage cryptographic key generation and re-encryption processes, enabling healthcare providers to access only the data explicitly authorized by the patient. All access requests, key operations, and permission updates are immutably recorded on a blockchain ledger, ensuring transparency, traceability, and accountability. The design further enforces forward and backward access control, automatically revoking past privileges when permissions are modified. Experimental evaluation demonstrates that the framework effectively ensures confidentiality, integrity, and access control while resisting tampering and supporting efficient real-time medical services, making it a promising solution for secure and scalable e-Health data exchange.
Apr 16, 2026·2026 4th International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)
Ayyana Prabu J, Ramkumar M. P, Emil Selvan G S R, Muthumeena S · 6 authors
The pervasive adoption of Electronic Health Records (EHR) has improved the competence of healthcare storage, management of patient data and retrieval. These increase the vulnerability of EHR records through data breaches, unauthorized access, and proliferation of insider threat behaviors, with limited compatibility of health records. To address this issue, blockchain-based systems were introduced; however, they encountered leakage of sensitive information and have limited scalability. Concerning this, a Multi-Layer Blockchain framework integrated with Zero-Knowledge Proof (ZKP) (MLB-ZKP) was proposed to secure the EHR systems by privacy-preserving verification and reduce sensitive data exposure. The multi-layer blockchain method ensures the data storage, secure transaction validation process and efficient access control process. The proposed framework integrates the ZKP mechanism to aid the secure authentication process and data verification process without revealing the sensitive information. The entire framework ensures the patientcentric data ownership, secure cross-institutional data sharing and a governing privacy protection process. To validate the effectiveness of the proposed framework, available benchmark datasets were used. The experimental results show that the proposed framework exhibits increased privacy level, reduced data exposure risk and storage efficiency.
With the growing volume of sensitive data stored and processed in cloud environments, conventional security models are no longer sufficient to guarantee privacy, integrity, and trust. This paper proposes a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) and homomorphic encryption (HE) to enable secure and privacy-preserving data sharing. ZKPs are employed to verify user access rights without exposing identities or underlying information, while HE allows computations to be performed directly on encrypted data, ensuring confidentiality is preserved throughout the data lifecycle. The proposed framework addresses the limitations of existing approaches that either lack encrypted computation capabilities or expose sensitive data during processing. Formal and informal analyses demonstrate the feasibility of the model in terms of encryption time, ZKP verification latency, and computation overhead. The framework is designed to be applied initially in the healthcare sector and aligns with national digital transformation initiatives such as Saudi Vision 2030.
Apr 15, 2026·2026 3rd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
Vallamkonda Jyothi, Dama Rishitha Naidu, C N Ravindra Kumar, Dunna Lalith Krishna Kanth · 5 authors
The accelerated digitalization of academic qualifications requires certificate authentication systems with not only the capability not to be tampered with, but also privacy-assuring and scalable. Although certificate verification using blockchain guarantees immutability and transparency, current solutions have high transaction costs, low scalability, and a loosely applied guarantee of privacy. In this research, proposes a new Privacy-Preserving and Scalable Blockchain-Based Certificate Authentication System, which combines Zero-Knowledge (ZK) rollups with an AI-based system of trust scoring. ZK-rollups save a lot of gas through batching certificate transactions and generating succinct cryptographic proofs, which enhances throughput and makes operational costs less. Zero-knowledge proofs can be used to facilitate privacy by providing the selective disclosure so that the verifiers can verify the validity of the certificates without access to sensitive personal information. Also, trust scoring model is an AI-based model that dynamically analyzes the actions of the validators to identify anomalies, collusion, and malicious actions. The experimental analysis shows significant reduction in transaction costs (as much as 80 percent), lower verification latency as well as resilience against Sybil and coordinated attacks. The suggested model provides a privacy-conscious, secure and scalable decentralized certificate authentication model.
Ethereum is a decentralised, censorship-resistant blockchain. It allows users to run decentralised applications and deploy Turing-complete smart contracts. However, the Ethereum blockchain is rather stagnant compared to Bitcoin and its ability to introduce new features to its chain. The Ethereum blockchain, unlike Bitcoin, has an Ethereum Virtual Machine (EVM) built into the chain and lets its users run decentralisation and deploy smart contracts, which fulfils one of the main ideas of blockchain. ERC-4337 introduces the possibility of using classic signature algorithms that are more efficient than ECDSA or multiparty computation and threshold signature schemes that further increase security. It also introduces post-quantum signature schemes in the blockchain. This change also allows the use of aggregators that can utilise Boneh-Lynn-Shacham signature schemes, which have also been used in the Ethereum beacon chain protocol. Also, making most of the in-use smart contracts deployed on the chain compatible with ERC-437 can be challenging as they must be upgraded or, if that is not possible, redeployed with code changes. In general, it can make blockchain interactions user-friendly without losing any of the major Ethereum blockchain principles. It laid the foundation of what Ethereum can become in the future, with Web2 functionality.