In view of the problems of false property rights and difficulties in identity authentication in intellectual property transactions, an identity authentication model for intellectual property transactions based on an alliance chain is proposed. Firstly, the two-factor identity authentication model's roles, functions, and processes are constructed. Secondly, the two-factor authentication mechanism of ID password combined with physiological and property rights features is proposed, the identity identification generation method of fingerprint biometrics and intellectual property features is established, and the constraint compression strategy based on Poseidon hash is designed to reduce the workload of zero-knowledge proof algorithm and realize the consistency of property rights identity. Finally, the security and performance analysis of the authentication model is carried out, and the comparison and validation of related models are carried out, which shows that the model has good security and reliability.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Multi-scalar multiplication (MSM) is crucial in cryptographic applications and computationally intensive in zero-knowledge proofs. MSM involves accumulating the products of scalars and points on an elliptic curve over a 377-bit modulus, and the Pippenger algorithm converts MSM into a series of elliptic curve point additions (PADDs) with high parallelism. This study investigates accelerating MSM on the Versal ACAP platform, an emerging hardware that employs a spatial architecture integrating 400 AI Engines (AIEs) with programmable logic and a processing system. AIEs are SIMD-based VLIW processors capable of performing vector multiply-accumulate operations, making them well-suited for multiplication-heavy workloads in PADD. Unlike simpler multiplication tasks in previous studies, cryptographic computations also require complex operations such as carry propagation. These operations necessitate architecture-aware optimizations, including intra-core dedicated coding style to fully exploit VLIW capabilities and inter-core strategy for spatial task mapping. We propose various optimizations to accelerate PADDs, including (1) algorithmic optimizations for carry propagation employing a carry-save-like technique to exploit VLIW and SIMD capabilities and (2) a comparison of four distinct spatial mappings to enhance intra- and inter-task parallelism. Our approach achieves a computational efficiency that utilizes 50.2% of the theoretical memory bandwidth and provides 568 speedup over the integrated CPU on the AIE evaluation board.
Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitledCommitment-basedAnonymousPayment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participants’ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE.
The Internet of Behaviors (IoB) is an emerging concept that utilizes devices to collect human behavior and provide intelligent services. Although some research has focused on human behavior analysis and data collection within IoB, the associated security and privacy challenges remain insufficiently explored. This article analyzes the security and privacy risks at different stages of behavioral data generating, uploading, and use while also considering the dynamic characteristics of user activity areas. Then, we propose a blockchain-based distributed IoB data storage and sharing framework, which is categorized into sensing, processing, and management layers based on these stages. To accommodate both identity authentication and behavioral privacy, zero-knowledge proofs are used in the sensing layer to separate the correlation between behavior and identity, which is further extended to a distributed architecture for cross-domain authentication. In the processing layer, an improved consensus protocol is proposed to enhance the decision-making efficiency of distributed IoB by analyzing the geographical and computational capability of the servers. In the management layer, user permission differences and the privacy of access targets are considered. Different types of behavior are modeled as corresponding relationships between keys, and fine-grained secure access is achieved through function secret sharing. Simulation results demonstrate the effectiveness of the proposed framework in multi-scenario IoB, with average consensus and authentication times reduced by 74 percent and 56 percent, respectively.
The syndrome decoding problem is one of the NP-complete problems lying at the foundation of code-based cryptography. The variant thereof where the distance between vectors is measured with respect to the Lee metric, rather than the more commonly used Hamming metric, has been analyzed recently in several works due to its potential relevance for building more efficient code-based cryptosystems. The purpose of this article is to present a zero-knowledge proof of knowledge for this variant of the problem.
This article examines the integration of blockchain technology with enterprise resource planning (ERP) systems to enhance supply chain transparency and data integrity. Blockchain technology creates immutable, decentralized digital ledgers that fundamentally transform how organizations track transactions, verify product authenticity and maintain regulatory compliance. The implementation architecture features multi-layered components, including consensus mechanisms, smart contracts, middleware integration, and application interfaces that connect seamlessly with existing ERP environments. Through end-to-end traceability, cryptographic verification, and fraud prevention mechanisms, blockchain addresses critical vulnerabilities in traditional supply chains across pharmaceuticals, food production, automotive, and luxury goods sectors. Smart contracts further automate complex business processes, particularly in procurement cycles, by converting traditional agreements into self-executing code. Despite significant benefits, organizations face integration complexities, scalability constraints, and change management challenges that must be addressed for successful adoption. Future developments include cross-chain interoperability, zero-knowledge proofs, artificial intelligence integration, and asset tokenization that will further enhance enterprise blockchain capabilities.
Anna Maria Fink, Johanna Nattenmüller, Stephan Rau, Alexander Rau · 10 authors
OBJECTIVES: This study evaluated the effect of enhancing a GPT-4 model with retrieval-augmented generation on its ability to diagnose and classify traumatic injuries based on radiology reports. MATERIALS AND METHODS: In this prospective proof-of-concept study, we used retrieval-augmented generation as a zero-shot learning approach to provide expert knowledge from the RadioGraphics top ten reading list for trauma radiology to the GPT-4 model, creating the context-aware TraumaCB. Radiological report findings of 50 traumatic injuries were independently generated by two radiologists. The performance of the TraumaCB compared to the generic GPT-4 was evaluated by three board-certified radiologists, assessing the accuracy and trustworthiness of the chatbot responses in the 100 reports created. RESULTS: The TraumaCB achieved 100% correct diagnoses, 96% correct classification, and 87% correct grading, outperforming the generic GPT-4 with 93% correct diagnoses, 70% correct classification, and 48% correct grading. TraumaCB sources consistently achieved a median rating of 5.0 for explanation and trust. Challenges encountered mainly involved traumatic injuries lacking widely accepted classification systems. CONCLUSION: Augmenting a commercial GPT-4 model with retrieval-augmented generation improves its diagnostic and classification capabilities, positioning it as a valuable tool for efficiently assessing traumatic injuries across various anatomical regions in trauma radiology. KEY POINTS: Question Retrieval-augmented generation has the potential to enhance generic chatbots with task-specific knowledge of emergency radiology. Findings The TraumaCB excelled in accuracy, particularly in injury classification and grading, and provided explanations along with the sources used, increasing transparency and facilitating verification. Clinical relevance The TraumaCB provides accurate, fast, and transparent access to trauma radiology classifications, potentially increasing the efficiency of image interpretation in emergency departments and enabling customized reports based on local or individual preferences.
Open access
Artificial Intelligence in Healthcare and Education
The advent of quantum computing poses an existential threat to contemporary cryptographic standards, particularly those securing decentralized blockchain networks and cloud infrastructures.Classical public-key cryptosystems such as RSA, ECC, and DH, which rely on factorization and discrete logarithm problems, are rendered obsolete by Shor's algorithm, necessitating the transition toward post-quantum cryptographic (PQC) solutions.This study explores the integration of PQC algorithms, including lattice-based, hash-based, code-based, multivariate, and isogeny-based cryptographic mechanisms, within blockchain-ledger technologies and cloud architectures to ensure long-term security against quantum adversaries.A comparative analysis is conducted to evaluate computational efficiency, key size implications, communication overhead, and security resilience under quantum attack models.The research highlights the adaptation of PQC within blockchain consensus mechanisms, smart contract execution, and cryptographic primitives such as digital signatures, zero-knowledge proofs, and secure multi-party computation (MPC).Additionally, it examines the impact of PQC on cloud security, addressing challenges in quantum-safe key exchange protocols, homomorphic encryption for secure computations, and cross-platform interoperability within hybrid quantum-classical cloud ecosystems.Real-world implementations and benchmarking data provide insights into the feasibility of large-scale adoption, shedding light on standardization efforts by NIST and industry consortia.The study concludes with future directions, emphasizing the need for efficient PQC algorithm optimization, lightweight cryptographic frameworks for IoT-driven blockchain applications, and scalable post-quantum identity management systems.By establishing quantum-resistant security frameworks, this research underscores the imperative need for early adoption to mitigate cryptographic vulnerabilities in the impending post-quantum era.
Jihye Choi, Sai Rahul Rachuri, Ke Wang, Somesh Jha · 5 authors
Federated Learning (FL) enables collaborative model training while keeping client data private. However, exposing individual client updates makes FL vulnerable to reconstruction attacks. Secure aggregation mitigates such privacy risks but prevents the server from verifying the validity of each client update, creating a privacy-robustness tradeoff. Recent efforts attempt to address this tradeoff by enforcing checks on client updates using zero-knowledge proofs, but they support limited predicates and often depend on public validation data. We propose SLVR, a general framework that securely leverages clients' private data through secure multi-party computation. By utilizing clients' data, SLVR not only eliminates the need for public validation data, but also enables a wider range of checks for robustness, including cross-client accuracy validation. It also adapts naturally to distribution shifts in client data as it can securely refresh its validation data up-to-date. Our empirical evaluations show that SLVR improves robustness against model poisoning attacks, particularly outperforming existing methods by up to 50% under adaptive attacks. Additionally, SLVR demonstrates effective adaptability and stable convergence under various distribution shift scenarios.
The growing power of large language models (LLMs) has revolutionized how people access and utilize information. Notably, the LLMs excel at performing fine-grained data representation, which facilitates precise retrieval of information. They also generate high-quality answers based on external references, enabling the production of useful knowledge. The recent introduction of reasoning models, like OpenAI O1 and DeepSeek R1, marks another leap forward, highlighting LLMs' ability to think progressively before delivering final answers. This breakthrough significantly improves the ability to address complex tasks, e.g., coding and math proofs. Inspired by this progress, we aim to develop similar capabilities for retrieval models, which hold great promise for tackling critical challenges in the field, including multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships. With this motivation, we propose a novel approach called O1 Embedder, which generates useful thoughts for the input query before making retrieval for the target documents. To realize this objective, we conquer two technical difficulties. First, we design a data synthesis workflow, creating training signals for O1 Embedder by generating initial thoughts from an LLM-expert and subsequently refining them using a retrieval committee. Second, we optimize the training process, enabling a pre-trained model to be jointly fine-tuned to generate retrieval thoughts via behavior cloning and perform dense retrieval through contrastive learning. Our approach is evaluated by comprehensive experiments, where substantial improvements are achieved across 12 popular datasets, spanning both in-domain and out-of-domain scenarios. These results highlight O1 Embedder's remarkable accuracy and generalizability, paving the way for the development of next-generation IR foundation models.
Traditional banking systems rely heavily on centralized data storage and identity verification processes, leading to security vulnerabilities and regulatory burdens. Zero-Knowledge Proof (ZKP) technologies offer a cryptographic solution that enables financial verification-such as identity authentication, balance confirmation, and transaction validation-without disclosing sensitive user data. This paper explores the integration of zk-SNARK and zk-STARK protocols, ZK-Rollup-based scalability solutions, and zkLedger applications within banking operations. It examines the role of selective disclosure mechanisms in critical areas such as KYC/AML compliance, balance verification, cross-border transactions, and regulatory audits. Furthermore, a four-layer architectural model is proposed to facilitate the seamless integration of ZKP-based frameworks into traditional banking infrastructures, addressing key aspects such as security optimizations, transaction costs, and scalability. Lastly, the potential of ZKP in regulatory compliance, its bridging role between DeFi and centralized banking, and the emergence of ZKP-driven next-generation banking services are discussed.
Distributed certification is a set of mechanisms that allows an all-knowing prover to convince the units of a communication network that the network's state has some desired property, such as being 3-colorable or triangle-free. Classical mechanisms, such as proof labeling schemes (PLS), consist of a message from the prover to each unit, followed by one round of communication between each unit and its neighbors. Later works consider extensions, called distributed interactive proofs, where the prover and the units can have multiple rounds of communication before the communication among the units. Recently, Bick, Kol, and Oshman (SODA '22) defined a zero-knowledge version of distributed interactive proofs, where the prover convinces the units of the network's state without revealing any other information about the network's state or structure. In their work, they propose different variants of this model and show that many graph properties of interest can be certified with them. In this work, we define and study distributed non-interactive zero-knowledge proofs (dNIZK); these can be seen as a non-interactive version of the aforementioned model, and also as a zero-knowledge version of PLS. We prove the following: - There exists a dNIZK protocol for 3-coloring with O(log n)-bit messages from the prover and O(log n)-size messages among neighbors. - There exists a family of dNIZK protocols for triangle-freeness, that presents a trade-off between the size of the messages from the prover and the size of the messages among neighbors. - There exists a dNIZK protocol for any graph property in NP in the random oracle models, which is secure against an arbitrary number of malicious parties.
Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.
<div> Mixnet protocols are used in electronic voting protocols to mix the ballot box before the tally, to preserve ballots privacy and unlinkabiliy. Whereas proving security properties of the other components of the electronic voting protocols has globally already been done in several logical frameworks and tools, proofs of mixnets remain a real challenge to handle. In this paper we focus on the quite recent CCSA logic, which enables handling of computational security proofs with first-order logics facilities. We enrich the logic to be able to deal with zero-knowledge proofs and rewinding techniques, and provide the first complete proof of Terelius-Wikström mixnet protocol. </div>
Nojan Sheybani, Anees Ahmed, Michel A. Kinsy, Farinaz Koushanfar
Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret itself. ZKPs have shown to be an extremely powerful tool, as evidenced in both industry and academic settings. In recent years, the utilization of user data in practical applications has necessitated the rapid development of privacy-preserving techniques, including ZKPs. This has led to the creation of several robust open-source ZKP frameworks. However, there remains a significant gap in understanding the capabilities and real-world applications of these frameworks. Furthermore, identifying the most suitable frameworks for the developers' specific applications and settings is a challenge, given the variety of options available. The primary goal of our work is to lower the barrier to entry for understanding and building applications with open-source ZKP frameworks. In this work, we survey and evaluate 25 general-purpose, prominent ZKP frameworks. Recognizing that ZKPs have various constructions and underlying arithmetic schemes, our survey aims to provide a comprehensive overview of the ZKP landscape. These systems are assessed based on their usability and performance in SHA-256 and matrix multiplication experiments. Acknowledging that setting up a functional development environment can be challenging for these frameworks, we offer a fully open-source collection of Docker containers. These containers include a working development environment and are accompanied by documented code from our experiments. We conclude our work with a thorough analysis of the practical applications of ZKPs, recommendations for ZKP settings in different application scenarios, and a discussion on the future development of ZKP frameworks.
Chhavi Yadav, Evan Monroe Laufer, Dan Boneh, Kamalika Chaudhuri
In principle, explanations are intended as a way to increase trust in machine learning models and are often obligated by regulations. However, many circumstances where these are demanded are adversarial in nature, meaning the involved parties have misaligned interests and are incentivized to manipulate explanations for their purpose. As a result, explainability methods fail to be operational in such settings despite the demand \cite{bordt2022post}. In this paper, we take a step towards operationalizing explanations in adversarial scenarios with Zero-Knowledge Proofs (ZKPs), a cryptographic primitive. Specifically we explore ZKP-amenable versions of the popular explainability algorithm LIME and evaluate their performance on Neural Networks and Random Forests. Our code is publicly available at https://github.com/emlaufer/ExpProof.
Zero-knowledge proof (ZKP) is an important cryptographic tool that sees wide applications in real-world scenarios where privacy must be protected, including privacy-preserving blockchains and zero-knowledge machine learning. Existing ZKP acceleration approaches using GPUs, FPGAs, and ASICs focus only on classic protocols that rely on expensive elliptic curve arithmetics. Emerging ZKP protocols based on hash functions can greatly reduce the algorithmic complexity, but they also introduce much more diverse computation kernels that cannot be efficiently handled by a single accelerator chip if dedicated units for each kernel are used. Our approach is to leverage a unified hardware architecture that is able to efficiently support the common primitives in ZKP, and then use smart mapping strategies to flexibly map various kernels to such hardware while ensuring high resource utilization. We design UniZK as such a ZKP accelerator, with a systolic-array-based hardware architecture enhanced with extra local links and a new vector processing mode. We propose novel mapping strategies to support diverse kernels including number theoretic transforms, hash functions, and general polynomial computations. UniZK provides 97x and 46x speedups on average compared to the CPU and GPU implementations of the same protocols, and is also 840x faster than previous ZKP accelerators using different protocols.
Faisal Yusuf, Riya Widayanti, Sausan Raihana Putri, Aulia Wellington
Blockchain technology has gained widespread adoption across various industries due to its decentralized and transparent nature. However, its inherent characteristics, such as immutability and openness, expose the system to critical security threats and privacy concerns. This study aims to develop a comprehensive framework to enhance blockchain security and privacy, addressing prevalent challenges such as data breaches, transaction anonymity, and resistance to cyberattacks. The proposed framework integrates advanced cryptographic techniques, including Zero Knowledge Proofs (ZKP), Secure Multi Party Computation (SMPC), and enhanced encryption protocols, alongside innovative consensus mechanisms to improve system robustness. A simulation-based evaluation and a real-world case study were conducted to validate the framework. The results demonstrate significant improvements in mitigating security vulnerabilities, such as 51% attacks and double spending, while ensuring enhanced privacy through anonymized data handling and confidentiality-preserving transactions. Furthermore, the case study confirmed the framework's practicality and adaptability across diverse applications. These findings highlight the proposed framework's potential to establish a secure and privacy-preserving blockchain ecosystem, offering a solid foundation for future research and implementation in both public and private blockchain networks.
It is becoming harder to manage the growing amounts of waste generated daily at an increasing rate. These problems require an efficient solution that guarantees effectiveness and transparency and maintains trust within the community. To improve the process of traditional waste management, we proposed a unique solution, “GREENLINK”, which uses a combination of blockchain technology with the concept of zero-knowledge proofs (ZKPs), non-fungible tokens (NFTs), and Walrasian equilibrium. Zero-knowledge proofs (cryptographic protocols) are used to verify organizations and prove compliance (e.g., certification, recycling capacity) without disclosing sensitive information. Through an iterative bidding process, the proposed framework employs Walrasian equilibrium, a technique to balance supply and demand, guaranteeing equitable pricing and effective resource distribution among participants. The transactions and waste management activities are securely recorded on an immutable ledger, ensuring accountability, traceability, and transparency. The performance of the proposed model is evaluated. Parameters like average latency, TPS, and memory consumption are calculated using Hyperledger Caliper (a blockchain performance benchmark framework).
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to reveal the signature and requires re-encoding new ones for code reuse, which potentially compromising the system's usability. To overcome these challenges, RoSeMary obtains high-quality watermarks by training the watermark insertion and extraction modules end-to-end to ensure (i) unaltered watermarked code functionality and (ii) enhanced detectability and robustness leveraging pre-trained CodeT5 as the insertion backbone to enlarge the code syntactic and variable rename transformation search space. In the deployment, RoSeMary uses zero-knowledge proofs for secure verification without revealing the underlying signatures. Extensive evaluations demonstrated RoSeMary achieves high detection accuracy while preserving the code functionality. RoSeMary is also robust against attacks and provides efficient secure watermark verification.
The work examines modern approaches to building electronic voting systems, such as blockchain, which promises to revolutionize the process due to its immutability and decen-tralization properties, as well as traditional cryptographic methods, including homomorphic encryption, which allows vote counting without the need to decrypt each individual vote. Blind signatures ensure the ability to confirm a vote without disclosing the user's identity, and zero-knowledge proofs allow voting without interacting with the server. The goal of the work is to select an approach for building electronic voting systems based on a comparative analy-sis of their key characteristics. The solved tasks include reviewing the requirements, general-ized structures, and main procedures of electronic voting systems; analyzing the existing types of electronic voting systems and their comparative characteristics. During the work, existing systems and other literature were thoroughly analyzed. The article provides a de-tailed analysis of the advantages and limitations of these technologies, as well as their suit-ability for different electoral systems, considering important aspects such as scalability, effi-ciency, and protection against potential threats. Throughout the work, a list of requirements for electronic voting systems was compiled, the main procedures present in electronic voting systems were outlined, a set of actors in typical electronic voting systems was defined, and the generalized structures of their main types were presented. A comparative analysis of the types of electronic voting systems based on compliance with the requirements was conducted. An approach was chosen for further system development.
Zero-knowledge proofs (ZKPs) are widely applied in digital economies, such as cryptocurrencies and smart contracts, for establishing trust and privacy between untrusted parties. Classical ZKPs rely on computational assumptions and are vulnerable to quantum attacks. While a recent advance suggests quantum-sound symmetric relativistic ZKPs for the graph three-coloring problem without computational assumptions, the high round complexity, which leads to unachievable runtime and overall randomness cost, renders them impractical for real-life deployment. To overcome this, we develop an efficient asymmetric relativistic ZKP protocol using relativistic bit commitments, and prove its quantum soundness by relating it to the nonlocal Clauser-Horne-Shimony-Holt (CHSH) game. Our protocol achieves a linear relationship between the round complexity and the number of edges, and thus significantly improves practical feasibility. In addition, we implement a proof-of-principle experiment which completes all interactive rounds in about 0.22 seconds and requires an overall randomness cost of 430.81 MB. Our work illustrates the powerful potential of integrating special relativity with quantum theory in trustless cryptography, paving the way for robust applications against quantum attacks in distrustful Internet environments. Zero-knowledge proofs can protect privacy online, but almost all current methods are vulnerable to quantum attacks. Here, the authors report an efficient relativistic protocol and experiment that resists quantum attacks and greatly reduces runtime, randomness cost and communication rounds.
The proliferation of Internet of Things (IoT) devices in smart environments has created unprecedented challenges in identity management and security. Traditional centralized identity management systems face scalability, privacy, and single-point-of-failure issues when applied to IoT ecosystems. This paper presents a novel blockchain-based framework for decentralized identity management in smart IoT environments. Our proposed framework leverages blockchain technology's immutable ledger, smart contracts, and cryptographic mechanisms to provide secure, scalable, and privacy-preserving identity management for IoT devices. The framework incorporates a multi-layered security architecture that includes device authentication, access control, and identity verification mechanisms. Experimental results demonstrate that our approach achieves 99.7% authentication accuracy with reduced latency compared to traditional centralized systems. The framework also provides enhanced privacy protection through zero-knowledge proofs and selective disclosure mechanisms. This research contributes to the advancement of secure IoT identity management and provides a foundation for future developments in decentralized IoT security