Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
ABSTRACT This study investigates an audit data privacy protection mechanism based on blockchain technology and constructs a secure and efficient computational model. The system is designed to support practical domains such as medical record systems and financial audit platforms, ensuring data integrity, traceability, and confidentiality. Leveraging a distributed ledger and optimized consensus mechanism, the model automates data sharing and audit processes through smart contracts. A hybrid encryption approach is proposed, integrating RSA algorithm with chaos theory to enhance encryption complexity and randomness. Experimental results—conducted on real‐world medical audit data—demonstrate that, compared to baseline methods, the proposed scheme improves privacy protection by up to 50%, increases ciphertext complexity by 45%, and reduces encryption and verification time by approximately 20 s. The system also supports up to 500 concurrent users with a throughput of 583.49 requests/s, indicating strong scalability and efficiency.
Advanced Steganography and Watermarking Techniques
Mohamed Abdessamed Rezazi, Mouhamed Amine Bouchiha, A. Bendada, Yacine Ghamri-Doudane
Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations-namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.
This research presents a unified and intelligent security framework for cloud computing environments by integrating Quantum Neural Networks (QNNs), Zero-Knowledge Proof Engines (ZKEs), and Blockchain technology. As the scale and complexity of cloud infrastructures grow, traditional security mechanisms are proving insufficient against advanced cyber threats such as Distributed Denial of Service (DDoS), malware attacks, and Man-in-the-Middle (MITM) exploits. The proposed framework leverages the computational power of quantum systems to enhance the performance of Deep Learning models, enabling faster and more accurate threat detection. ZKEs provide privacy-preserving authentication by verifying user identities without revealing sensitive information, while Block chain ensures data integrity, decentralization, and tamper-proof transaction records. Experimental evaluationconducted using IBM Qiskit and a honey net-integrated Deep Neural Network (DNN) demonstrates a significant improvement in detection accuracy, reduced response time, and enhanced system resilience compared to conventional approaches. The results validate the effectiveness of the multi-layered model in addressing real-world cloud security challenges. This research contributes a scalable, privacy-centric, and quantum-secure architectural foundation for the next generation of cloud-based systems
D Abisha, Ashwanth Raj A, Sunil Kumar S, Senthil Kumar M J · 5 authors
The exponential growth of data driven applications in domains such as AI, healthcare, and IoT demands secure, transparent, and decentralized platforms for sharing high quality datasets. Traditional centralized systems suffer from opaque pricing, privacy risks, single points of failure, and lack of verifiable ownership, limiting trust and scalability. This paper proposes AquaStream, a decentralized data marketplace that leverages blockchain, the InterPlanetary File System (IPFS), and Ethereum smart contracts to tokenize datasets as non-fungible tokens (NFTs), ensuring verifiable ownership and tamper-proof traceability. The system employs dual-layer encryption (AES-256/RSA) to safeguard data during storage and transmission, while IPFS provides distributed storage to improve availability and reduce on-chain storage costs. Smart contracts based on ERC-20 and ERC-721 standards automate secure payments and access control, eliminating intermediaries and enhancing transparency. A React.js-based interface with MetaMask integration enables intuitive dataset management for both technical and non-technical users. Experimental evaluation on the Ethereum Sepolia testnet and Polygon Mumbai network demonstrates significant performance gains, including up to 73.6% reduction in gas costs using Layer 2 scaling and dataset retrieval latency as low as 4.2 seconds. These results confirm AquaStream’s efficiency, cost-effectiveness, and scalability. By bridging the gap between data providers and consumers, AquaStream fosters a collaborative ecosystem that promotes data ownership, privacy, and equitable monetization, offering a robust foundation for future decentralized data economies.
Web3 applications require execution platforms that maintain confidentiality and integrity without relying on centralized trust authorities. While Trusted Execution Environments (TEEs) offer promising capabilities for confidential computing, current implementations face significant limitations when applied to Web3 contexts, particularly in security reliability, censorship resistance, and vendor independence. This paper presents dstack, a comprehensive framework that transforms raw TEE technology into a true Zero Trust platform. We introduce three key innovations: (1) Portable Confidential Containers that enable seamless workload migration across heterogeneous TEE environments while maintaining security guarantees, (2) Decentralized Code Management that leverages smart contracts for transparent governance of TEE applications, and (3) Verifiable Domain Management that ensures secure and verifiable application identity without centralized authorities. These innovations are implemented through three core components: dstack-OS, dstack-KMS, and dstack-Gateway. Together, they demonstrate how to achieve both the performance advantages of VM-level TEE solutions and the trustless guarantees required by Web3 applications. Our evaluation shows that dstack provides comprehensive security guarantees while maintaining practical usability for real-world applications.
Mohammed Ziaul Haider, Tayyaba Noreen, Mishah Uzziél Salman, Marcos Dias de Assunção · 5 authors
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
M Ramya, B S Anuvarshini, G Kanika, A Lohith · 5 authors
The identity verification is required by many digital platforms, it raises real privacy and security risks when users are required to submit identity documents multiple times. When identity documents are stored in centralized databases data breaches or misuse from within the organization holding the data may occur. To mitigate risks associated with centralized data storage models, a decentralized identity management system is being created using blockchain and distributed file storage system architecture. When users submit identity documents, the data is stored securely in a decentralized file storage system and a cryptographic hash is generated that will be used to reference the data at a later time. This hash will then be recorded on a blockchain that is immutable and creates a secure log. Identification is verified through a manual verification process by authorized reviewers who approve or reject submissions all while the user is able to monitor submission status. User identity privacy is maintained by using zero-knowledge proofs to verify distinct attributes of identity that are required without revealing the identity data itself. With proof verification attaining a success rate of $98.6 \%$, admin validation accuracy of $96.4 \%$, and gas optimization leading to a $\mathbf{3 1 \%}$ decrease in execution costs in comparison to baseline smart contract calls, the system showed outstanding operational efficiency. This will provide users with more control over their identity documents while also providing secure, transparent and privacy preserving identity verification to be used in modern digital services.
The contemporary pursuit of technology integration in commerce has taken a promising pathway in complex systems like decentralized systems. Once seen as a technology facing risks and shortcomings, blockchain and its associated projects have been on a steady global rise as a decentralized systemic relay. Blockchain’s role in enhancing trust in decentralized systems is significant. The technology enables secure storage and transfer of information in a tamper-proof manner. The transparency of blockchain systems, with their distributed ledger maintained by peer nodes, is a central feature. Analyzing transparency in blockchain aligns with calls for more responsible research and innovation that considers long-term societal impacts. Discussions on blockchain’s feasibility, sustainability, and societal implications remain ongoing. Trust models for blockchain-based systems rely heavily on transparency to ensure the trustworthiness of information and mitigate dishonest behavior. Despite its rapid growth, many remain unfamiliar with blockchain’s mechanisms and potential. By understanding its applications, blockchain’s real-world potential becomes more apparent. Its ability to streamline processes, enhance security, reduce costs, and remove intermediaries gives it transformative power. Blockchain promises increased efficiency, transparency, and privacy, potentially revolutionizing finance, healthcare, supply chains, voting systems, and intellectual property. This chapter explores blockchain’s transformative impact and the critical role of transparency in fostering trust.
The convergence of artificial intelligence (AI), multi-access edge computing (MEC), and cloud environments has transformed identity governance by enabling real-time decision-making and seamless access control across decentralized infrastructures. However, this evolution has also introduced complex challenges concerning data privacy, identity trust, and security. This review explores privacy-first security models that integrate AI for identity governance in hybrid cloud-edge architectures. It evaluates privacy-preserving techniques such as homomorphic encryption, federated learning, and zero-knowledge proofs, emphasizing their role in ensuring secure identity authentication, authorization, and auditability. The paper critically analyzes the limitations of conventional identity and access management (IAM) frameworks in dynamic, resource-constrained edge environments and proposes adaptive models that embed privacy by design. Furthermore, the review investigates the interplay between explainable AI (XAI) and policy enforcement for transparent and compliant identity governance. By synthesizing advancements in cryptographic methods, AI reasoning engines, and decentralized identity (DID) systems, the paper outlines a roadmap for building secure, scalable, and privacy-compliant identity infrastructures in the era of pervasive computing. Keywords: Privacy-Preserving Identity Governance, AI-Driven Access Control, Multi-Access Edge Computing (MEC). Federated Identity Management, Explainable AI (XAI), Zero-Knowledge Proofs.
Omar Jarkas, Ryan K. L. Ko, Naipeng Dong, Redowan Mahmud
Integrity verification and attestation are critical in containerized environments, where traditional Linux Integrity Measurement Architecture (IMA) falls short due to its lack of container-specific contextualization. These gaps undermine container autonomy, escalate privacy risks, and impede granular integrity checks. Addressing these challenges, this paper introduces the Virtual IMA (VIMA), a novel framework that refines Linux IMA’s principles to support containerized settings. Using nested Merkle trees, VIMA’s Two-Tree Architecture (2TA) enables detailed integrity assessments across system-wide monolithic trees and individual container trees. Integrating Merkle and zero-knowledge (ZK) proofs establishes VIMA as a secure, privacy-preserving verification and attestation solution. Our comparative analysis and initial prototype testing reveal that VIMA significantly improves upon traditional IMA with minimal performance overhead, offering substantial scope for optimization.
Md. Rafid Haque, Sakibul Islam Munna, Sabbir Ahmed, Md. Tariqul Islam · 6 authors
Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency, compromising the real-time collaboration essential for modern workflows. This study introduces a novel hybrid architecture combining the security of the Ethereum blockchain and the InterPlanetary File System (IPFS) with two key contributions: 1) Shamir's Secret Sharing (SSS) to create a trust-minimized model for key distribution, and 2) an authoritative-first, optimistic-fallback retrieval protocol utilizing a temporary middleware to decouple the user experience from blockchain confirmation delays. We implemented a full prototype and conducted a comprehensive performance evaluation on the public Sepolia testnet. Our results demonstrate that this architecture not only provides a secure, auditable, and resilient platform for source code hosting but also achieves highly competitive user-perceived performance. Our user-perceived push time reduces submission latency by up to 49% compared to a standard git push for common repository sizes, proving that a well-designed decentralized VCS can balance the core tenets of security and decentralization with the practical need for speed and efficiency.
The looming occurrence of large scale quantum computing presents a significant danger to the mathematical basis of cryptography that forms the foundation of the currently-deployed blockchain networks, particularly those networks that operate within distributed cloud-based systems. Existing blockchain models based on traditional cryptographic algorithms like RSA and ECC can be broken using quantum computing, thus new models built around the concept of quantum-resistance should be developed. This article proposes a new framework of Post Quantum Secure Blockchain that combines enhanced lattice-based cryptography with more computation time consensus protocol, especially designed to work effectively in cloud-based systems. The suggested architecture utilizes NTRU and Ring-LWE lattice primitives to sign transactions, verify them and to certify the integrity of the block such that transactions and block cannot be corrupted, even by a quantum adversary. To resolve the scalability and energy consumption challenges, a dynamically adjusted time based on consensus of the computing - extends Trusted Execution Environments (TEEs) to support dynamically adaptive block proposal timings depending on real time network conditions in terms of latency, throughput, and node workload. The system is also using lightweight hash chain validation to further simplify computational complexity, and to enhance the transaction throughput. Results gained in the experimental studies within the setup emulating a cloud context show that with the proposed model, the transaction latency and energy consumption scales down considerably in contrast to the traditional Proof-of-Work and Proof-of-Stake systems without jeopardising security assurances. The given research builds a scalable, future-ready blockchain framework that can be used in next-generation decentralized applications in the fields of finance, healthcare, and IoT, overcoming the quantum-security and high-performance barrier between quantum security and real-world, high-performance cloud deployment.
In the rapidly evolving landscape of blockchain technology, the twin challenges of scalability and security remain significant obstacles to widespread adoption. Traditional blockchain architectures struggle to balance the increasing demands for transaction throughput and the imperative of maintaining robust security measures. This work addresses these limitations by proposing an innovative model that integrates advanced privacy mechanisms, rigorous security analysis, and scalability enhancements to forge a more resilient and efficient blockchain framework. The cornerstone of our model is the introduction of ZeroKnowledge Proofs (ZKPs) to enhance user privacy significantly. By enabling transaction verification without revealing sensitive information, ZKPs mitigate information leakage and boost transaction confidentiality. Our findings suggest an estimated 15% improvement in privacy levels, marking a substantial advancement over existing methods that often compromise user privacy for transparency. Addressing the security aspect, we employ Temporal Logic of Actions Plus (TLA+) for formal verification of the blockchain protocol. This method allows us to model the blockchain's behavior systematically, ensuring its correctness, safety, and liveness even under adverse conditions such as Byzantine faults. Our analysis reveals a 98% success rate in detecting and thwarting Byzantine behaviors, thereby substantiating the robustness of our proposed model against a range of security threats. To tackle the issue of scalability, we introduce adaptive sharding with dynamic load balancing. This approach not only partitions the network into manageable shards but also optimizes transaction processing by adapting to changes in transaction volume and network congestion. Our results prove a 20% increase in transaction throughput and a 25% decrease in network latency, showcasing the effectiveness of adaptive sharding in enhancing blockchain scalability and performance.
Aiming at the problems of insufficient integrity assurance, high risk of privacy leakage, and low cross-departmental circulation efficiency in the field of government data security, this study proposes an optimized blockchain solution integrating an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and dynamic Attribute-Based Encryption (ABE). By constructing a government data security storage model, the data layer's hash verification mechanism (SHA-256 + Merkle Patricia Tree) and network layer's P2P protocol (with packet loss retransmission) are optimized based on typical government blockchain platforms (e.g., Nanjing Electronic License Sharing Platform). Experiments with 200 nodes simulated government networks, comparing TPS, data verification delay, and privacy protection before and after optimization. The results show that after optimization: TPS increased by 37.2% (from 89.6 to 123.0), cross-departmental data verification time was shortened to 0.42 seconds, and zero-knowledge proof was used to realize anonymized query of sensitive fields (such as ID card numbers and real estate information). The research indicates that this technical solution can effectively resolve the contradictions among integrity, privacy, and circulation of government data, providing technical references for the engineering implementation of government blockchain systems.
This paper presents a comprehensive literature review on the application of blockchain technology in ensuring data integrity and security within decentralized applications (dApps). Blockchain, with its inherent features such as immutability, decentralization, and transparency, offers a robust framework for safeguarding data across various sectors, including finance, healthcare, and supply chain management. Through an extensive qualitative analysis of existing studies, this research explores how cryptographic techniques, consensus mechanisms, and blockchain's distributed nature contribute to securing data in decentralized environments. The review examines key findings from the literature, including the integration of advanced cryptographic methods such as zero-knowledge proofs and homomorphic encryption, which enhance data privacy while maintaining integrity. Furthermore, the study discusses the challenges of scalability, energy consumption, and off-chain data security in blockchain-based systems, identifying areas for future research. The review also highlights the importance of hybrid blockchain models and scalable consensus algorithms in addressing the limitations of current blockchain frameworks. Overall, this paper contributes to the growing body of knowledge on blockchain-based data security and integrity in decentralized applications and offers recommendations for further research to enhance the scalability, efficiency, and environmental sustainability of blockchain systems.
This chapter proposes a Trustworthy Federated Identity Management (TFIM) framework developed to overcome user identity management challenges across multiple blockchain networks. The research work focuses on solving key issues, such as the lack of unified trust frameworks, insufficient cross-chain identity verification, and limited privacy-preserving mechanisms for cross-chain data sharing. TFIM allows secure DApp interoperability by assessing participant trustworthiness across blockchain networks with the integration of zero-knowledge proofs and federated learning techniques. Performance evaluation shows that TFIM processes 44,041 transactions/s with 15% degradation under real-world conditions, supports 1000 concurrent users, and handles 100 cross-chain authentications/s across up to 15 interconnected networks. Although TFIM provides better privacy protection and more advanced cross-chain capabilities than the Blockchain-Based Federated Identity Framework (BFIF), it comes at the cost of computational overhead and registration/authorization speed. The results suggest potential directions for future optimization while maintaining TFIM’s robust cross-chain functionality and privacy features.
Blockchain-based systems ensures data immutability and traceability, making them well suited for decentralized content governance. However, conventional metadata anchoring strategies remain static and task-agnostic, resulting in suboptimal trade-offs between auditability, privacy, and scalability. To address this limitation, we propose a Task-Driven Dynamic Metadata Mapping (TDMM) mechanism that adapts anchoring strategies based on task semantics and user roles. TDMM classifies metadata into three distinct anchoring types: full on-chain anchoring for audit-critical tasks, selective disclosure via zero-knowledge proofs (ZKPs) for sensitive attributes, and off-chain reference anchoring for general-purpose metadata. A dedicated mapping controller dynamically routes metadata fields to the appropriate anchoring mode according to predefined task policies. To preserve privacy without sacrificing verifiability, TDMM incorporates a hybrid ZKP architecture that processes proofs off-chain while anchoring verification results on-chain. We implement TDMM on a Hyperledger Fabric network, augmented with IPFS and Circom-based ZKP tooling. Experimental results show that TDMM significantly reduces on-chain storage overhead, lowers re-identification risk, and supports task-appropriate latency and throughput trade-offs, demonstrating its effectiveness in balancing transparency, privacy, and scalability in decentralized metadata governance.
The rapid development of large language models (LLMs) has significantly propelled the development of artificial intelligence (AI) agents, which are increasingly evolving into diverse autonomous entities, advancing the LLM-based multi-agent systems (LaMAS). However, current agentic ecosystems remain fragmented and closed. Establishing an interconnected and scalable paradigm for Agentic AI has become a critical prerequisite. Although Agentic Web proposes an open architecture to break the ecosystem barriers, its implementation still faces core challenges such as privacy protection, data management, and value measurement. Existing centralized or semi-centralized paradigms suffer from inherent limitations, making them inadequate for supporting large-scale, heterogeneous, and cross-domain autonomous interactions. To address these challenges, this paper introduces the blockchain-enabled trustworthy Agentic Web (BetaWeb). By leveraging the inherent strengths of blockchain, BetaWeb not only offers a trustworthy and scalable infrastructure for LaMAS but also has the potential to advance the Web paradigm from Web3 (centered on data ownership) towards Web3.5, which emphasizes ownership of agent capabilities and the monetization of intelligence. Beyond a systematic examination of the BetaWeb framework, this paper presents a five-stage evolutionary roadmap, outlining the path of LaMAS from passive execution to advanced collaboration and autonomous governance. We also conduct a comparative analysis of existing products and discuss key challenges of BetaWeb from multiple perspectives. Ultimately, we argue that deep integration between blockchain and LaMAS can lay the foundation for a resilient, trustworthy, and sustainably incentivized digital ecosystem. A summary of the enabling technologies for each stage is available at https://github.com/MatZaharia/BetaWeb.