This research explores the distributed database security storage and access control scheme based on IPFS and blockchain for the privacy issues such as sensitive data leakage and account security under the rapid development of Internet technology. The research background focuses on the contradictory status quo of data value enhancement and black-market data trading in the fields of intelligent medical care and unmanned driving, etc. Although the existing database security technology has made progress in encryption algorithms, dynamic protection, etc., it is still faced with the challenges of performance bottleneck and fine-grained access control of centralized architecture. The research aims to integrate the advantages of IPFS distributed storage and the tamper-proof characteristics of blockchain to construct a new type of secure storage system. Through theoretical analysis of IPFS peer-to-peer file system architecture, blockchain six-layer model (data layer, network layer, consensus layer, etc.) and AES/SM4 encryption algorithms, a system solution integrating blockchain smart contract and IPFS storage is designed: SM4 encrypts the original data and then stores it in IPFS, and achieves traceability through the blockchain record hash, and introduces the proxy re-encryption based on the identity technology to Realize dynamic access control. Experiments comparing the performance of MongoDB and IPFS show that in 5000 transactions, the delay of IPFS mode 12 nodes is reduced by 1.71 times compared with 6 nodes, which is significantly better than that of MongoDB's by 1.22 times; in the throughput test, IPFS increases linearly with the increase of nodes, while MongoDB decreases after the peak value. The study confirms that the combination of IPFS and blockchain can effectively reduce transaction latency by 31%, improve throughput by 30%, and safeguard the security of the whole data lifecycle through cryptographic technology. The results provide a decentralized security framework for distributed databases, with both theoretical innovation and engineering application value, which is of great practical significance for highly sensitive data fields such as healthcare and finance.
With the continuous development of blockchain technology, massive off-chain data is mapped on the blockchain, ensuring the authenticity and privacy of on-chain data and off-chain data is a significant challenge. To solve this question, many studies use oracle to provide secure and reliable data for blockchain applications. Existing oracle schemes can protect the privacy of single-source data and prove the authenticity of private data sources to the third party. However, when handling multi-source data, these schemes require multiple executions to process and verify all data. We propose an optimized oracle scheme based on the “TLS-MPC” framework to improve efficiency. Firstly, we optimize the handshake process by dividing the$\mathrm{n}$servers into$\mathrm{t}$clusters and use the session ticket to reduce the number of MPC executions during the three-party handshake. As a result, most servers within each cluster run a fast three-party handshake by session ticket. Secondly, the prover runs two-party computation with the verifier to generate the queries and sends them to each data source to get the multi-source data. Then we design a constructable zero-knowledge proof system. Prover will inputs the multi-source data into the system to generate the proof value with a joint computation circuit. Finally, verifier will check the result sent from prover after the zero-knowledge proof is completed. Comparing with the DECO, our scheme is more efficient.
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. Our design includes: (1) a comprehensive metadata schema for cross-model compatibility, (2) a stake-weighted validation mechanism to align incentives, and (3) a token economy that rewards contributors proportionally to their impact. The proposed architecture demonstrates how decentralized systems could potentially match centralized alternatives in efficiency while providing superior ownership guarantees and censorship resistance through blockchain-anchored provenance tracking. By decoupling prompts from specific AI models or outputs, this work establishes the foundation for an open ecosystem of human-AI collaboration in the Web3 era, representing the first systematic treatment of prompts as standalone digital assets with dedicated decentralized infrastructure.
Victoria L. Lemieux, Rosa Gil, Faith Molosiwa, Qizheng Zhou · 8 authors
As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.
Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 61% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6x faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.
LLMs have brought new, amazing abilities for understanding language, generating it and making decisions. Yet, there are serious concerns about data privacy, the ability to scale LLMs and how different components of a cloud-native system interact. The paper outlines a new Federated Data Modelling (FDM) framework specifically for making use of LLMs in secure and efficient distributed cloud settings. The framework achieves decentralized training, prevents data being leaked and meets the requirements of data residency laws by using federated learning and dynamic schema harmonization with container orchestration. Moreover, the proposed FDM technique relies on zero-trust security, confidential computing and Kubernetes-native operations to provide isolation, watching and traceability among the various tenants. On typical benchmark datasets, the approach shown here performs better in terms of privacy, how quickly the model learns and how quickly it may be used in practice compared to centralized training. By using this study, AI service providers can ensure their LLM service is trustworthy and safe for IAP use in healthcare, finance and government.
Patrick Spiesberger, Jan Droll, Hannes Hartenstein
In the Ethereum system, the exclusion of specific transactions is currently feasible with minimal effort due to a power imbalance among entities. This censorship opportunity threatens the dependability of time-sensitive services deployable on Ethereum. In this paper, we look at this threat from an access control perspective and attribute it to a lack of accountability for censorship, a lack of policy definition and enforcement, as well as to the lack of disincentivization of policy violation. We propose an approach to enforceable policies in Ethereum. Furthermore, we demonstrate how a specific policy can address the shortcomings of existing censorship mitigation techniques, particularly Inclusion Lists. Under the assumption that block assemblers are unwilling to incur significant financial penalties as well as that the local view on the network messages is sufficiently consistent, the proposed approach guarantees the inclusion of a transaction in a block within 27 seconds in a non-saturated network. The empirical validation of sufficiently consistent views on outstanding transactions is currently in progress.
Barbara Guidi, Andrea Michienzi, Leonardo Pasquale
The InterPlanetary File System (IPFS) is among the most well-known distributed file systems applied in Web3 applications, where data storage is involved. Non-Fungible Tokens (NFTs) represent one of the main scenarios in which distributed content sharing is enacted via IPFS. Generally, NFTs are created, sold and bought via so-called marketplaces, easing the technical difficulties. They play a crucial role during the sale of NFTs because they act as intermediaries between users and underlying technologies. In this paper, we evaluate the impact of NFT marketplaces on the availability of NFT resources in the IPFS network. We focus on the relationship between resources and marketplaces, trying to understand whether access to some of the resources related to NFTs is transparent and consistent with the information present in the contract. We selected $\mathbf{1 0}$ collections and 3 NFTs for each collection and monitored these assets on IPFS. For the monitoring phase, we deployed our node and requested providers of the assets every six hours for two weeks. Our analysis shows that IPFS does not guarantee high availability and marketplaces refer to copies of NFTs, thus violating the integrity and security guaranteed by NFTs and IPFS.
The Inter Planetary File System (IPFS) is a decentralized, peer-to-peer file storage and sharing protocol designed to make the web faster, more open, and resilient. It allows users to store and share files in a distributed network without relying on central servers, similar to a BitTorrent-based approach, but with a few unique optimization features. In contrast to traditional HTTP, which fetches data from a single location (typically a server), IPFS retrieves data from multiple nodes holding the content. This can improve access speeds and ensure availability even if some nodes go offline. In this paper we discuss, the evolution of IPFS, key features, prime applications and future challenges. The results and concluding sections discuss the potential application areas and uniqueness offered by the storage related blockchain projects. An estimated of 10 billion USD is the market capitalization of the storage projects across top rankers which is estimated to rise exponentially over the next decade.
Jun 25, 2025·2025 IEEE/ACIS 29th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)
This paper proposes MetaCropX, a smart agriculture framework that introduces a dual-token design to represent and track field-level events as they occur. Upon detecting a real-time crop event, a fungible AgriTrack Token (ATT) is minted and incrementally updated using stack-based metadata appends, maintaining an immutable record of disease status, treatments, and environmental conditions. Role-Based Access Control (RBAC) enforced via smart contracts ensures that only authorized stakeholders contribute metadata. At the end of the crop cycle, selected ATTs are converted into a single non-fungible AgriProof Asset (APA) token consisting of the verified crop lifecycle. On the other hand, off-chain InterPlanetary File System (IPFS) storage reduces on-chain data load and supports efficient metadata management. Simulated crop scenarios validate transitions, role enforcement, and metadata integrity. The proposed framework enhances transparency and traceability by allowing the stakeholders to verify the crop data in a secure manner.
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a “space-for-time” technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based largenumber modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $\lt sup\gt1\lt/sup\gt|.65 \times$ and $3 \times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of 128 and 8,192, respectively.
Abstract—File storage platforms face inherent challenges such as censorship, limited transparency, vulnerability to single points of failure, and restricted user control over data. To address these limitations, this paper proposes a decentralized file-sharing system that integrates the Ethereum blockchain with the InterPlanetary File System (IPFS). Our design leverages smart contracts to securely manage file metadata and enforce access controls, providing an immutable and tamper-resistant record of data ownership and permissions. IPFS is utilized for efficient, distributed file storage, enhancing scalability and availability. User authentication is handled through wallet-based cryptographic verification, eliminating reliance on centralized identity providers. Additionally, the system supports micropayment- based monetization via smart contracts, enabling direct and transparent transactions between content creators and consumers. The proposed platform delivers a secure, censorship-resistant, and user-empowered file-sharing environment consistent with the principles of Web3. Keywords- Blockchain, IPFS, Smart Contracts,
The European Union's (EU) data strategy aims to create a single market for seamless data flow while ensuring proper governance, privacy, and data protection. In this paper, we present SEDIMARK, an EU project, that builds on this strategy by developing a fully decentralised, secure data marketplace. The goal of SEDIMARK is to build a complete toolbox that enables users to purchase and process data assets. The toolbox includes tools for data cleaning, decentralised machine learning models and secure data exchange. SEDIMARK offers users full control over data assets by enabling them to keep their data locally and thus removing the need for central servers. With customisable pipelines and tools, SEDIMARK supports a wide range of users, from novices to experts, promoting seamless collaboration and fair access to high-quality datasets across Europe. The decentralised connectivity in SEDIMARK is achieved with the use of Distributed Ledger Technology (DLT). Furthermore, SEDIMARK's architecture features a unique Connector component using Self Sovereign Identities (SSI), fostering trust and secure interactions. Transactions in SEDIMARK are stored in a Registry, a decentralised, immutable, non-repudiable and permissionless database. Together the technologies used in SEDIMARK ensure privacy, trust and data quality for secure management, sharing, and monetisation of assets in data spaces.
Ricardo Lopes Almeida, Fabrizio Baiardi, Damiano Di Francesco Maesa, Laura Ricci
Non-Fungible Tokens (NFTs) are promising additions to the blockchain universe. After Ethereum introduced the NFT concept in 2015, it quickly rose in popularity, triggering other public blockchains to support their own versions of these new types of token by extending their smart contract capabilities to enable minting of these new tokens in their ecosystems. Ethereum is the reference for NFT development, establishing the token standards widely used today. The popularity of NFTs in Ethereum created a rich application ecosystem that also revealed limitations in scalability and throughput for this network. An attempt to solve these issues resulted in the creation of Flow, a new NFT-centric blockchain that claims to solve the scalability issues that plagued Ethereum. This paper presents the alternative NFT architecture introduced by Flow and how it compares with Ethereum’s version.
Scalability remains a major challenge in blockchain technology, particularly for Layer-1 networks like Ethereum, where high transaction volumes cause congestion and high gas fees. Zero-Knowledge (ZK) Rollups have emerged as scalable Layer-2 solutions, offering enhanced security and lower transaction costs. However, existing zk-Rollups such as zkSync Era, dYdX, and StarkNet each have their advantages and limitations in terms of efficiency, cost, and decentralization. This paper proposes zkFusion, a hybrid zk-Rollup protocol that combines the most effective features of these rollups to achieve faster transaction rates, improved cost-effectiveness, and enhanced security. zkFusion employs a flexible proof mechanism, optimized transaction batching, and a modular data throughput method, allowing users to balance between cost and security. Additionally, it integrates a decentralized sequencer to enhance network reliability and mitigate centralization risks. Comparative evaluation with existing zk-Rollups demonstrates that zkFusion significantly reduces transaction fees, improves throughput, and maintains high security assurances. By blending the innovations of zkSync Era, dYdX, and StarkNet, zkFusion aims to set a new benchmark for scalability, efficiency, and cost reduction in Layer-2 blockchain solutions.
By bringing digital artworks to one’s everyday shopping list, NFTs have revolutionized a large group’s understanding of art. In this article, I challenge the stance presented by authors such as Taylor and Sloane that NFTs shift the consumption of objects to the liquid consumption of the non-material by drawing on the aesthetic identity of NFTs as images. Firstly, I analyse the evolution of blockchain products’ function from that of strictly fungible tokens to non-fungible goods. NFTs rely on art’s specificity and acquire their new identity by virtue of entering the art market, which does not happen with ordinary cryptocurrencies. NFTs thus benefit from the contextualization of art as a socially and technologically mediated immaterial process: they emerge as instances of the artification of a non-artistic immaterial experience and become art-like. However, an NFT as a commodity is not pure code; it does have an image attached. This distinguishes NFTs from particular fungible tokens that hold added value due to their unique historical context. I further point out that the fact that NFTs have, as commodities, taken the form of digital images is an expression of a pervasive need for interaction with objects in today’s everyday digital experience. Also, the shift of NFTs from being just “specific” phenomena as art objects to “singular” phenomena construed as authors of the ways in which they will be perceived and understood is discussed. NFTs create their own impact, on the one hand, independently of their often poor artistic or aesthetic quality, and on the other hand, as entirely dependent on their aura as mathematically and ontologically unique, visually perceptible entities.
Cross-chain data sharing in decentralized storage networks faces challenges in security and performance, including data breaches and high latency. This paper proposes a layered protocol integrating decentralized storage and cross-chain communication to address these issues. The protocol ensures robust security and optimizes performance through sharding, encryption, and concurrency. Experimental results show significant improvements in throughput and latency compared to baseline schemes, with manageable storage overhead. This study offers a secure, efficient solution for cross-chain data sharing, with applications in decentralized finance and distributed computing. Future work will focus on high-load optimization and real-world validation.
Abstract Digital identity data management is a significant challenge for businesses and individuals who wish to interact online in the increasingly digital economy, government, and society. Most recently, non-fungible tokens (NFTs) have been proposed as a solution to represent digital identities and underlying data. NFTs are stored on a blockchain based data management system and seem useful for representing digital identity data stored in independent data systems. The idea of using NFTs to represent digital identities and their associated data is promising, but it also raises significant concerns regarding data privacy and compliance. This article examines the NFT-enabled digital identity data representation and management, highlighting associated privacy risks and mitigation strategies. This study employs a qualitative desk research approach, reviewing industry reports, academic papers, and policy documents to analyze trends, technological advancements, and regulatory considerations. It concludes with recommendations for leveraging programmable privacy to address these challenges, providing valuable insights for researchers and practitioners in privacy-preserving digital identity and NFT-enabled identity management.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks.Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements.Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures.To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method.Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure.Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods. CCS Concepts Security and privacy File system security; Management and querying of encrypted data.
This paper explores the dynamics of multibranch forging algorithms within Proof-of-Stake (PoS) systems, focusing on the tails switching effect and its associated risks, such as the Nothing-at-Stake (N@S) attack. The study introduces a series of modifications to the traditional single-branch approach, drawing inspiration from quantum mechanics to enhance system security and efficiency. By analyzing the impact of smooth and direct measure functions on the cumulative measure of blockchain branches, the paper demonstrates that direct measures effectively reduce tails switching and stabilize the network. Additionally, the introduction of single-branch nodes is shown to stabilize the best chain, reducing the likelihood of history rewrites. The results suggest a promising pathway for improving multibranch PoS systems, emphasizing the need for further investigation into delayed propagation, transaction processing, and measure combinations.
Esam Mahdi, Carlos Martín-Barreiro, Xavier Cabezas
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting. We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed index. We evaluate the performance of our proposed model by comparing it with four other machine learning models: two are non-sequential feedforward models: Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN), and two are bidirectional sequential memory-based models: Bidirectional Long-Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The performance of the model is assessed using several metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), along with statistical validation through the nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test. The results demonstrate that our hybrid model consistently achieves superior accuracy, highlighting its effectiveness for financial prediction tasks. These findings provide valuable insights for improving real-time decision making in cryptocurrency markets and support the growing use of hybrid deep learning models in financial analytics.
With the rapid growth of the Internet of Things (IoT), maintaining data integrity, confidentiality, and authentication is now an imperative challenge. Most conventional cryptographic solutions cannot satisfy the specific constraints of IoT environments, which include limited computational resources, energy efficiency, and scalability. This study proposes a lightweight hybrid cryptographic framework combining Authenticated Encryption with Associated Data (AEAD) and Verifiable Random Functions (VRF) with Elliptic Curve Digital Signature Algorithm (ECDSA). The hybrid framework is intended to offer robust data integrity, secure authentication, and efficient encryption mechanisms with minimal computational overhead. Our solution makes use of AEAD (AES-GCM or ChaCha20-Poly1305) in order to establish both confidentiality and integrity within a single encryption process and with much less processing time than in traditional approaches such as AES-CTR with HMAC. Use of VRF guarantees that cryptographic algorithms result in verifiable randomness that increases replay attack and unauthorized entry security. ECDSA is utilized for lightweight digital signatures, providing non-repudiation without the computational overhead being higher than RSA-based integrity mechanisms. To ensure the efficacy of our methodology, we performed thorough benchmarking tests comparing AEAD + VRF + ECDSA with conventional cryptographic methods like AES-CTR + HMAC and integrity verification based on RSA. It is revealed by our benchmarks that our hybrid solution considerably cuts down encryption time, minimizes CPU utilization, and maximizes memory usage, thus being very suitable for resource-poor IoT devices. In contrast to AES-CTR + HMAC, which needs independent encryption and authentication phases, AEAD's hybrid approach has the least storage footprint and computational overhead. Furthermore, avoiding a dedicated verification step (necessary in HMAC-based designs) adds to system responsiveness. Our work adds to the literature through a scalable, effective, and secure cryptographic framework optimized for IoT use cases such as secure messaging, sensor data encryption, and access control in distributed systems. Real-world deployment in IoT platforms, post-quantum cryptographic augmentation, and implementing zero-knowledge proofs (ZKPs) for improved privacy-preserving authentication are next steps. By solving major problems in IoT security, our hybrid approach provides an efficient yet reliable alternative to state-of-the-art cryptographic solutions to guarantee end-to-end data confidentiality and integrity within contemporary IoT infrastructures.
Emerging cryptographic systems such as Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKP) are computation- and data-intensive. FHE and ZKP implementations in software and hardware largely rely on the von Neumann architecture, where a significant amount of energy is lost on data movements. A promising computing paradigm is computing in memory (CIM) which enables computations to occur directly within memory thereby reducing data movements and energy consumption. However, efficiently performing large integer multiplications - critical in FHE and ZKP - is an open question, as existing CIM methods are limited to small operand sizes. In this work, we address this question by exploring advanced algorithmic approaches for large integer multiplication, identifying the Karatsuba algorithm as the most effective for CIM applications. Thereafter, we design the first Karatsuba multiplier for resistive CIM crossbars. Our multiplier uses a three-stage pipeline to enhance throughput and, additionally, balances memory endurance with efficient array sizes. Compared to existing CIM multiplication methods, when scaled up to the bit widths required in ZKP and FHE, our design achieves up to 916x in throughput and 281x in area-time product improvements.