Secure Multi-party Computation (MPC) considers the problem where a set of mutually distrusting parties want to jointly compute a function over their private inputs, without revealing any extra information about these inputs other than what it can be inferred from the output of the function. This setting is well-motivated, and it has many real-world applications such as auction, voting, etc. MPC can be also seen as a generalization of many natural cryptographic primitives. For example, zero-knowledge (ZK) can be viewed as a special case of two-party secure computation. In ZK, a party, called prover aims to convince a second party, called verifier, that the prover’s private input witness w and a public input statement x belong to a relation R. An important research direction in secure computation is to find the trade-off between the required setup (e.g., the use of the broadcast channel, the use of common reference string (CRS) / public key infrastructure (PKI), the upper bound of the parties that can be corrupted, etc.), and the security guarantees that can be achieved. The setups can be viewed as some general assumptions that the protocol needs to satisfy, and they influence the usability of the protocol in real-world scenarios. In principle, having simpler (or no) setups mean that the protocol is more general and can be more useful in real-world scenarios. At the same time, having simpler setups may lead to weaker security guarantees. Therefore, finding the trade-off between setup and security guarantees is important and meaningful. In this thesis, we target MPC and ZK, and we focus on how to minimize the setup for MPC and ZK while still providing meaningful levels of security. More specifically: Regarding MPC, we focus on the dishonest majority (i.e., the adversary can corrupt all but one party), and we aim at 1) minimizing the use of broadcast channels. 2) studying the MPC with pre-processing when no setup is available. • Informally, a broadcast channel guarantees that when a message is sent, this reaches all the parties, without ambiguity. It also guarantees that if an honest party receives a message from a corrupted party, then it is guaranteed that all the honest parties have received the message. To realize broadcast, parties in the protocol could run the broadcast protocol, which may require many rounds of peer-to-peer communications. An alternative way is to rely on physical or external infrastructure such as blockchain. In both cases, broadcast is expensive, as such, we want to minimize its use. In particular, this thesis presents the following results: – When assuming no setup, we give a complete characterization with respect to the use of broadcast channels, and we obtain the optimal results. – We consider the same problem for the case that we only want to allow the black-box use (i.e., do not have access to the code of the algorithm) of the oblivious transfer protocol. We also give a characterization. • In the standard definition of MPC, the parties’ private inputs are fixed before the start of the protocol. However, there is another type of MPC named MPC with pre-processing, where the protocol can pre-compute some messages without using parties’ inputs, and these messages can accelerate computations in the online phase (i.e., other computations that require parties’ inputs). Since some expensive computations can be pre-computed, the online phase could be more lightweight. Therefore, we want to remove the dependency of the input from as many rounds as possible, so that we can do some pre-processing. In this direction, we explore the protocol with no setup. We provide a compiler that can turn a big class of MPC protocol that may require the inputs already to compute the first round, into a new protocol that needs the inputs only in the last two rounds. We also propose new MPC definitions that capture this delayed-input features. Regarding ZK, we do the following: • In standard single-theorem ZK definition, the security of the ZK protocol is guaranteed to hold only when one proof is issued. In the case where multiple zero-knowledge proofs need to be issued (i.e., to prove multiple NP statements), each new zero-knowledge proof requires a freshly generated setup. In the multi-theorem ZK definition, instead, one setup is sufficient for generating multiple zero-knowledge proofs for multiple instances. We propose a multi-theorem protocol (in the format of a compiler) that follows the Fiat-Shamir paradigm and relies on correlation intractable hash functions. Moreover, our protocol remains zero-knowledge and sound even against adversaries that choose the statement to be proven (and the witness for the case of zero-knowledge) adaptively on the key of the hash function. Prior works could achieve this adaptive security only inefficiently via NP reductions. • ZK protocols are secure only when all setups are correctly generated, but in real-world scenarios, some of the setups may not be correctly generated. For instance, to run a non-interactive zero-knowledge (NIZK) protocol, the setup CRS could be chosen with bias. In this case, the security of the NIZK protocol does not hold anymore. Instead of finding a secure ZK candidate, one alternative solution is to have multiple instantiations of ZK candidates and assume that only for a subset of them the setup is generated correctly. More formally, we consider the case where only a subset of the instances are secure. In more detail, given access to n candidate instantiations of a NIZK for some language, we want to have a construction that itself implements a NIZK for the same language without relying on any additional computational assumptions. We refer to this type of construction as combiner, and the combiner is secure assuming at least t of the given candidates are secure. In this work, we provide three different constructions of robust NIZK combiners and show that combiners are impossible to realize unless the majority of the input candidates are secure.
Contemporary Online Social Networks (OSNs) present critical vulnerabilities in user authentication and data integrity protocols. Since the social network is a multi-user platform, it requires a well-performing authentication mechanism that works along with the blockchain to ensure secure transactions. The existing methodologies exhibit significant limitations, particularly susceptibility to quantum cryptanalysis and privacy vector compromises. This study proposes a novel blockchain-based framework for decentralized OSNs, implementing smart contracts and InterPlanetary File System (IPFS) protocols to establish a distributed authentication architecture that mitigates these vulnerabilities while maintaining computational efficiency. Initially, we propose a post-quantum digital signature followed by a blockchain system using the signatures. Unlike previous OSNs, our solution uses post-quantum approaches, making it secure against both classical and quantum attacks. To enhance the data authentication of social network users, this research leverages the post-quantum multimodal biometric-based approach, where an improved version of Crystals Dilithium 3 is utilized in place of ECDSA in the XRP Ledger (XRPL) blockchain. We have integrated different post-quantum algorithms with XRPL using the open quantum safe library (liboqs) and compared the results in terms of resource consumption. The research explores the advantages of the proposed approach, highlighting its potential to mitigate the shortcomings of conventional methods and ensure secure data transmission in the era of quantum computing.
Prof. C. V. Nalawade, V. S. Bhosale, S. A. Khetre, K. B. Kadam
In recent years, the convergence of Artificial Intelligence (AI), Blockchain, and Biometric technologies has revolutionized digital identity verification and secure electronic voting systems. The demand for transparent, tamper-proof, and fraud-resistant voting mechanisms has prompted researchers to explore distributed ledgers combined with intelligent authentication systems. This paper presents a comprehensive survey of AI-enabled blockchain voting architectures integrating biometric verification. The study explores technical advancements from 2020 to 2025 in decentralized voting, intelligent fraud detection, and privacy-preserving biometric mechanisms. We classify recent research trends, analyze current methodologies, and identify open challenges and future directions in implementing scalable and trustworthy e-voting frameworks. The analysis concludes that integrating blockchain’s immutability with AI-driven fraud detection and biometric identification ensures high security, transparency, and reliability in modern digital democracies.
Cloud infrastructure refers to the dynamic provisioning of computing resources over the internet, enabling scalable and flexible enterprise operations. However, such environments face significant security challenges, particularly in access management. Cloud infrastructure delivers scalable computing resources, yet traditional Identity and Access Management (IAM) mechanisms face challenges such as centralized control, misconfigurations, and limited auditability. This study addresses these challenges by proposing a Hyperledger Fabric-based decentralized access control framework integrated with Amazon Web Services (AWS) for healthcare data security. The framework employs Zero Knowledge Proof (ZKP) for identity validation, Ciphertext Policy Attribute Based Encryption (CP- ABE) with Proxy Re-Encryption (PRE) for fine-grained data access, and machine learning driven anomaly detection for continuous monitoring. Experimental evaluation achieved throughput of 15000 transactions per second, latency of 350 milliseconds, privacy preservation up to 99.1 percent, and anomaly detection accuracy of 99.63 percent, surpassing prior models significantly. Storage analysis demonstrated predictable scalability with encrypted medical records up to 20 MB, while token revocation time remained within 1.9 seconds under network stress. The results confirm that blockchain-based access control enhances security, privacy, and auditability while maintaining operational efficiency. This research establishes a scalable and tamper-resistant model for secure healthcare data management in cloud environments.
Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assume human authorization, but autonomous, agent-led payments remove that safeguard. This paper presents a blockchain-based framework that cryptographically authenticates and verifies the intent of every AI-initiated transaction. The proposed system leverages decentralized identity (DID) standards and verifiable credentials to establish agent identities, on-chain intent proofs to record user authorization, and zero-knowledge proofs (ZKPs) to preserve privacy while ensuring policy compliance. Additionally, secure execution environments (TEE-based attestations) guarantee the integrity of agent reasoning and execution. The hybrid on-chain/off-chain architecture provides an immutable audit trail linking user intent to payment outcome. Through qualitative analysis, the framework demonstrates strong resistance to impersonation, unauthorized transactions, and misalignment of intent. This work lays the foundation for secure, auditable, and intent-aware autonomous economic agents, enabling a future of verifiable trust and accountability in AI-driven financial ecosystems.
Prof. S. H. Thengil, Tanmay Sadanshiv, A. M. Patil, Shreyash Trimbake · 5 authors
Abstract - With the increasing volume of digital evidence in law-enforcement and judicial processes, ensuring integrity, traceability and tamper-resistance has become paramount. This paper presents the Blockchain Evidence Archive System (BEAS), a decentralized application that leverages blockchain technology, smart contracts and the InterPlanetary File System (IPFS) to provide a secure, immutable and transparent evidence- management platform. Evidence metadata is stored on an Ethereum-based blockchain while the associated large files (images, videos, documents) are stored on IPFS with their cryptographic hashes recorded on-chain. Role-based access control ensures only authorized users such as police officers and court officials can upload, verify or access evidence. We describe the system architecture, implementation details, security features and evaluate the performance of the system in terms of upload time, verification latency and resistance to tampering. The results demonstrate that BEAS significantly improves evidence integrity and auditability when compared to conventional centralized systems. We conclude with a discussion on future enhancements including biometric integration, mobile accessibility and enterprise-scale deployment. l Key Words: Blockchain Technology, IPFS, Digital EvidenceManagement, Decentralized Application, Smart Contracts, Ethereum Network, Cryptographic Hashing, Data Integrity, Tamper- Proof Storage, Role-Based Access Control, Chain of Custody, Evidence Verification, Immutable Ledger, Secure File Storage, Decentralized Architecture, Forensics Technology, Law Enforcement Data Security, Distributed Ledger Technology
With the advancement of the information age, the widespread application of electronic evidence in fields such as justice and finance has brought new challenges. Although existing blockchain electronic evidence sharing schemes have immutability and transparency, they still have shortcomings in access control, data privacy protection, and efficiency. In addition, traditional attribute encryption strategies lack effective revocation mechanisms and cannot fully protect privacy when implementing fine-grained access control. Therefore, in order to address the above limitations, a blockchain electronic evidence sharing scheme based on an improved ciphertext policy attribute encryption combined with zero knowledge proof technology has been proposed. The research innovatively introduces revocable ciphertext strategy encryption, which addresses the security risks caused by decryption key leakage through revocation function, ensuring the secure storage and sharing of electronic evidence. Meanwhile, the study also improved the PBFT consensus algorithm to enhance its performance in handling large volumes of transactions. The results showed that the storage TPS of the research model reached 492, and the query TPS reached 655. The computational cost of improving the PBFT consensus algorithm is 1.94 × 10 4 , and the maximum computational cost of the electronic evidence access control model based on zero knowledge proof is 509. Compared with traditional blockchain based electronic evidence sharing methods, the improved method not only enhances storage and sharing efficiency, but also further strengthens privacy protection capabilities by combining zero knowledge proof technology. In summary, the research method effectively achieves secure sharing and privacy protection of electronic evidence on blockchain, providing support and reference for electronic evidence storage in fields such as justice and finance. However, there are still challenges in terms of scalability and data storage in the research, so algorithms can be optimized in the future to further improve the application scope of the system.
List decoding of codes can be seen as the generalization of unique decoding of codes while list decoding over finite fields has been extensively studied, extending these results to more general algebraic structures such as Galois rings remains an important challenge. Due to recent progress in zero knowledge systems, there is a growing demand to investigate the proximity gap of codes over Galois rings in Yizhou Yao(2025). The proximity gap is closely related to the decoding capability of codes. It was shown in Eli Ben-Sasson(2020) that the proximity gap for RS codes over finite field can be improved to $1-\sqrt{r}$ if one consider list decoding instead of unique decoding. However, we know very little about RS codes over Galois ring which might hinder the development of zero knowledge proof system for ring-based arithmetic circuit. In this work, we first extend the list decoding procedure of Guruswami and Sudan to Reed-Solomon codes over Galois rings, which shows that RS codes with rate $r$ can be list decoded up to radius $1-\sqrt{r}$. Then, we investigate the list decoding of folded Reed-Solomon codes over Galois rings. We show that the list decoding radius of folded Reed-Solomon codes can reach the Singlton bound as its counterpart over finite field. We also extend the deterministic pruning method of Vikrant Ashvinkumar(2026) to Galois rings, showing how to prune the affine free module obtained from the linear-algebraic decoder and recover the candidate codewords. Finally, we improve the list size of our folded Reed-Solomon code to $O(1/\varepsilon^2)$ by extending recent work in Shashank Srivastava(2025) to Galois Rings. By developing the recent work of Yeyuan Chen(2025), we show that folded Reed-Solomon codes over Galois rings satisfy the relaxed generalized Singleton bound in the average-radius sense with optimal list size $O(1/\varepsilon)$.
Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limitations significantly impact performance. Computing-in-Memory (CiM) is a promising approach to tackle this problem. However, existing schemes currently suffer from two main problems: 1) Most works focus on low bit-width modular multiplication, which is inadequate for mainstream cryptographic algorithms such as elliptic curve cryptography (ECC) and the RSA algorithm, both of which require high bit-width operations; 2) Recent efforts targeting large number modular multiplication rely on inefficient in-memory logic operations, resulting in high scaling costs for larger bit-widths and increased latency. To address these issues, we propose LaMoS, an efficient SRAM-based CiM design for large-number modular multiplication, offering high scalability and area efficiency. First, we analyze the Barrett's modular multiplication method and map the workload onto SRAM CiM macros for high bit-width cases. Additionally, we develop an efficient CiM architecture and dataflow to optimize large-number modular multiplication. Finally, we refine the mapping scheme for better scalability in high bit-width scenarios using workload grouping. Experimental results show that LaMoS achieves a $7.02\times$ speedup and reduces high bit-width scaling costs compared to existing SRAM-based CiM designs.
Abstract In today’s era of digital transformation, online transactions have become vital to financial systems, e-commerce, and decentralized applications. However, increasing dependence on digital payment infrastructures has also raised major security concerns such as hacking, identity theft, and unauthorized access. To address these challenges, the proposed project “Blockchain Secure Transaction” presents a decentralized framework that ensures transparency, integrity, and confidentiality in digital transactions. The system uses blockchain technology to record and validate each transaction in a distributed ledger, eliminating centralized control and making data immutable and tamper-proof. The workflow begins with user registration, where users provide details and set a picture password for secure recognition. During login, the system verifies credentials and performs biometric authentication to confirm user identity. Unregistered users are redirected to the registration page, maintaining process integrity. Once authenticated, users access the dashboard to initiate secure transactions. To preserve privacy, Zero-Knowledge Proof (ZKP) is used, allowing users to prove transaction authenticity without revealing sensitive information. Transactions then pass through smart contract verification, which ensures compliance with predefined conditions. Successful verifications result in completed transactions, while suspicious or invalid ones are blocked or frozen automatically. All user data and transaction logs are securely stored in Firebase, with backend processing handled in Java and the frontend designed using React (app.jsx). By combining blockchain’s immutability, smart contract automation, ZKP privacy proofs, and biometric authentication, the Blockchain Secure Transaction System offers a multi-layered, tamper-resistant, and transparent solution for secure online payments — enhancing trust and reliability in the digital economy.
Kassem Danach, Abbas Tarhini, Wael Hosny Fouad Aly, Hussin Hejase
Blockchain technology relies on cryptographic mechanisms for transaction security and data integrity. However, the growing computational complexity, high transaction costs, and scalability issues pose significant challenges to blockchain adoption. Traditional cryptographic methods—such as hashing, key generation, encryption, and decryption—introduce excessive computational overhead, leading to energy inefficiencies and increased latency. This research proposes an optimization-driven crypto analysis framework that integrates metaheuristic algorithms, combinatorial optimization, reinforcement learning, and game theory to enhance the efficiency and security of blockchain cryptographic processes. The framework focuses on optimized cryptographic computation, gas fee reduction in smart contracts, security enhancement against cryptanalysis, and improved scalability of consensus mechanisms. Experimental evaluations demonstrate up to 39.4\% reduction in cryptographic execution time, 29.4\% savings in smart contract gas fees, and 33.3\% improvement in decentralization of Proof-of-Stake validators. These results validate the effectiveness of the proposed framework in achieving secure, scalable, and cost-efficient blockchain operations.
Rana Alaa, Darío González-Ferreiro, Carlos Beis-Penedo, Manuel Fernández‐Veiga · 6 authors
Split learning is an approach to collaborative learning in which a deep neural network is divided into two parts: client-side and server-side at a cut layer. The client side executes its model using its raw input data and sends the intermediate activation to the server side. This configuration architecture is very useful for enabling collaborative training when data or resources are separated between devices. However, split learning lacks the ability to verify the correctness and honesty of the computations that are performed and exchanged between the parties. To this purpose, this paper proposes a verifiable split learning framework that integrates a zk-SNARK proof to ensure correctness and verifiability. The zk-SNARK proof and verification are generated for both sides in forward propagation and backward propagation on the server side, guaranteeing verifiability on both sides. The verifiable split learning architecture is compared to a blockchain-enabled system for the same deep learning network, one that records updates but without generating the zero-knowledge proof. From the comparison, it can be deduced that applying the zk-SNARK test achieves verifiability and correctness, while blockchains are lightweight but unverifiable.
Shashank Motepalli, N. Garg, Gengrui Zhang, Hans‐Arno Jacobsen
Geospatial decentralization is essential for blockchains, ensuring regulatory resilience, robustness, and fairness. We empirically analyze five major Proof of Stake (PoS) blockchains: Aptos, Avalanche, Ethereum, Solana, and Sui, revealing that a few geographic regions dominate consensus voting power, resulting in limited geospatial decentralization. To address this, we propose Geospatially aware Proof of Stake (GPoS), which integrates geospatial diversity with stake-based voting power. Experimental evaluation demonstrates an average 45% improvement in geospatial decentralization, as measured by the Gini coefficient of Eigenvector centrality, while incurring minimal performance overhead in BFT protocols, including HotStuff and CometBFT. These results demonstrate that GPoS can improve geospatial decentralization {while, in our experiments, incurring minimal overhead} to consensus performance.
Mohammed Abdelhamid Nedioui, Ali Khechekhouche, Konstantinos Κarampidis, Giorgos Papadourakis · 5 authors
The integration of artificial intelligence (AI) and blockchain technology opens new avenues for decentralized, transparent, and secure data-driven systems. However, ensuring privacy and verifiability in collaborative AI environments remains a key challenge, especially when model updates or decisions must be recorded immutably on-chain. In this paper, we propose a novel privacy-preserving framework that leverages an ElGamal-based aggregate signature scheme with aggregate public keys to enable secure, verifiable, and unlinkable multi-party contributions in blockchain-based AI ecosystems. This approach allows multiple AI agents or data providers to jointly sign model updates or decisions, producing a single compact signature that can be publicly verified without revealing the identities or individual public keys of contributors. The design is particularly well-suited to resource-constrained or privacy-sensitive applications such as federated learning in healthcare or finance. We analyze the security of the scheme under standard assumptions and evaluate its efficiency in different terms. The study and experimental results demonstrate the potential of our framework to enhance trust and privacy in AI collaborations over decentralized networks.
Flavio Corradini, Alessio Galassi, Alessandro Marcelletti, Barbara Re
Blockchain has been largely adopted in several sectors through decentralized applications. These rely on smart contracts, whose implementation can diverge from the intended logic, leading to unexpected behaviors. Such behaviors can be identified by observing the data produced within smart contracts’ execution, employing compliance checking techniques. Their adoption poses a main limitation since the traditional rule specification languages do not explicitly refer to the blockchain. To address this limitation, we propose a domain-specific language, called CoBlock , to define compliance rules by relying on blockchain characterizations as a first-class citizen. This enables a tailored framework for compliance checking, supporting the definition of rules to check smart contract execution data. The framework is implemented as a web application. We demonstrate and evaluate the applicability of the language and the accuracy and feasibility of the framework using two real-world decentralized applications, namely Augur and PancakeSwap.
The rapid evolution of blockchain technology has revolutionized digital asset ownership through NonFungible Tokens (NFTs). NFTs enable creators to tokenize unique digital assets such as art, music, and collectibles, ensuring authenticity, transparency, and verifiable ownership. This research paper presents the design and development of a decentralized NFT Marketplace using Solidity Smart Contracts and Pinata IPFS (InterPlanetary File System) integration. The proposed system eliminates the need for intermediaries by leveraging blockchain-based automation, allowing creators to mint, list, and sell NFTs securely while maintaining full ownership control. The marketplace integrates MetaMask wallet authentication for secure transactions and employs Solidity smart contracts to handle NFT minting, transfer, and royalty distribution on the Ethereum blockchain. Additionally, Pinata IPFS provides decentralized storage for digital media and metadata, ensuring data permanence and tamper-proof accessibility. The system architecture combines transparency, security, and user-friendliness, empowering creators with fair compensation and buyers with verifiable proof of ownership. Experimental implementation results demonstrate that the proposed NFT Marketplace provides a reliable, transparent, and scalable environment for digital asset exchange. This study highlights the potential of decentralized systems in reshaping the digital economy and sets the foundation for future enhancements such as multi-chain support, AI-based recommendations, and mobile integration.
Blockchain technology has emerged as a transformative solution for decentralized and secure data management. However, the security of blockchain networks heavily relies on robust cryptographic protocols. This article provides a comprehensive analysis of key cryptographic techniques employed in blockchain security, including hash functions, digital signatures, consensus algorithms, and zero-knowledge proofs. We evaluate their roles in ensuring data integrity, authentication, confidentiality, and resistance to common attacks such as double-spending and Sybil attacks. A comparative study highlights the strengths and limitations of these protocols, guiding future enhancements for blockchain security frameworks
The rising importance of cryptocurrencies as financial assets pushed their applicability from an object of speculation closer to standard financial instruments such as loans. In this work, we initiate the study of secure protocols that enable fiat-denominated loans collateralized by cryptocurrencies such as Bitcoin. We provide limited-custodial protocols for such loans relying only on trusted arbitration and provide their game-theoretical analysis. We also highlight various interesting directions for future research.
The rapid expansion of multi-cloud ecosystems has intensified the demand for privacy-preserving analytics across untrusted infrastructures. This paper proposes Federated Zero-Trust Analytics (FZTA), a framework that integrates federated learning, zero-trust security, and privacy-enhancing computation to enable secure data collaboration without centralized trust. The design combines continuous identity verification, decentralized policy enforcement, and hybrid cryptography based on homomorphic encryption and differential privacy. Evaluation across three commercial clouds demonstrates that FZTA achieves near baseline model accuracy (within 2% of centralized training) while maintaining (ε<1.2, δ=10−5) differential privacy guarantees and less than 20% computational overhead. The framework resists eavesdropping, replay, and model inversion attacks while meeting compliance standards such as GDPR and HIPAA. Results confirm that strong privacy and federated scalability can coexist under zero-trust conditions, establishing a foundation for secure cross-domain analytics in healthcare, finance, and IoT applications.
Internet of Things (IoT) is transforming traditional agriculture into a more efficient, sustainable, and data-driven industry. By connecting various devices and sensors across the farm, IoT enables real-time monitoring, control, and optimization of agricultural processes. However, there are many security issues to deal with. IoT devices in precision farming collect sensitive data such as soil moisture, nutrient levels, and livestock health information. Unauthorized access to farm data can result in data theft, manipulation, or misuse. This can compromise the integrity of farming operations and potentially harm the environment. In this paper, we explore the mathematical foundations and practical implementations of model aggregation in federated learning (FL), with a particular focus on integration with distributed ledger technologies (DLT). We present a comprehensive analysis of aggregation algorithms, their convergence properties, and security guarantees. Additionally, we survey existing tools and platforms that facilitate federated learning deployments and examine how blockchain technology can address key challenges in federated learning systems including trust, incentive mechanisms, and auditability. Our analysis demonstrates that the combination of federated learning with blockchain creates a robust, transparent, and decentralized machine learning systems suitable for privacy-sensitive applications across precision farming, healthcare etc.
Behnam Khayer, Siamak Mirzaei, Hooman Alavizadeh, Ahmad Salehi Shahraki
Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem.
Quantum computing threatens foundational cryptographic assumptions in today’s distributed ledgers, while application demands outgrow the throughput and latency ceilings of single-chain blockchains. Directed acyclic graph (DAG) ledgers unlock parallelism but raise new questions about ordering, security, and light-client viability. This position paper argues for a postquantum (PQ) DAG ledger that matches DAG concurrency with PQ-secure consensus and transactions, plus a privacy-preserving identity/reputation layer. We sketch the architecture, situate it against the literature, and enumerate some open challenges to be addressed for deployment at scale. A carefully engineered PQ DAG can provide credible security and performance in a quantum-enabled adversarial landscape.