Blockchain Papers

Gu Zhimin, Guo Jing, Li Yan, Zhu Daohua · 6 authors

No abstract is available for this record.

Hyeonhak Kim, Sanjin Park

No abstract is available for this record.

Yu Cao, Xuehui Du, Lulu Zhang, Shihao Wang

With the continuous development of blockchain technology, an increasing number of scholars have begun to consider the harm of data leakage during on-chain transactions and the requirement for privacy data protection. Zero-knowledge range proof, as a cryptographic technology, can perform legitimacy verification of data while hiding private data, effectively realizing the protection of private data on the blockchain, so it is increasingly used to protect blockchain privacy. The mainstream construction methods for range proofs can be mainly divided into two categories: n-ary decomposition and square decomposition. This paper introduces and analyzes the advantages and disadvantages of these construction methods in detail. Then, based on these two methods, a zero-knowledge range proof scheme based on multibit split square decomposition (ZKRPMSSD) is proposed, which requires no trusted third-party setting and can achieve range proofs for arbitrary ranges. The proposed ZKRPMSSD scheme processes the original data based on the multibit split idea, and the acquisition method of secret value components is optimized so that the acquisition of components does not depend on the scale of the original problem. Additionally, the algorithms for proof generation and verification in the ZKRPMSSD scheme are redesigned based on the \(\Sigma\) protocol and Pedersen commitments, effectively reducing the computational cost of the proof generation and verification process. Finally, typical n-ary decomposition and square decomposition zero-knowledge range proof construction schemes are taken for comparative analysis. Under 256-bit security and the same problem scale, experimental results indicate that ZKRPMSSD has advantages in proof and verification time costs.

Usman Shahid, Shamsa Kanwal, Mahwish Bano, Saba Inam · 6 authors

Data security during transmission over public networks has become a key concern in an era of rapid digitization. Image data is especially vulnerable since it can be stored or transferred using public cloud services, making it open to illegal access, breaches, and eavesdropping. This work suggests a novel way to integrate blockchain technology with a Chaotic Tent map encryption scheme in order to overcome these issues. The outcome is a Blockchain driven Chaotic Tent Map Encryption Scheme (BCTMES) for secure picture transactions. The idea behind this strategy is to ensure an extra degree of security by fusing the distributed and immutable properties of blockchain technology with the intricate encryption offered by chaotic maps. To ensure that the image is transformed into a cipher form that is resistant to several types of attacks, the proposed BCTMES first encrypts it using the Chaotic Tent map encryption technique. The accompanying signed document is safely kept on the blockchain, and this encrypted image is subsequently uploaded to the cloud. The integrity and authenticity of the image are confirmed upon retrieval by utilizing blockchain's consensus mechanism, adding another layer of security against manipulation. Comprehensive performance evaluations show that BCTMES provides notable enhancements in important security parameters, such as entropy, correlation coefficient, key sensitivity, peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), and number of pixels change rate (NPCR). In addition to providing good defense against brute-force attacks, the high key size of [Formula: see text] further strengthens the system's resilience. To sum up, the BCTMES effectively addresses a number of prevalent risks to picture security and offers a complete solution that may be implemented in cloud-based settings where data integrity and privacy are crucial. This work suggests a promising path for further investigation and practical uses in secure image transmission.

Adil Marouan, Morad Badrani, Abderrahim Zannou, Nabil Kannouf · 5 authors

No abstract is available for this record.

Saijun Mo, Wenlong Feng, Mengxing Huang, Siling Feng · 6 authors

In view of the problems of false property rights and difficulties in identity authentication in intellectual property transactions, an identity authentication model for intellectual property transactions based on an alliance chain is proposed. Firstly, the two-factor identity authentication model's roles, functions, and processes are constructed. Secondly, the two-factor authentication mechanism of ID password combined with physiological and property rights features is proposed, the identity identification generation method of fingerprint biometrics and intellectual property features is established, and the constraint compression strategy based on Poseidon hash is designed to reduce the workload of zero-knowledge proof algorithm and realize the consistency of property rights identity. Finally, the security and performance analysis of the authentication model is carried out, and the comparison and validation of related models are carried out, which shows that the model has good security and reliability.

Ruisi Zhang, Neusha Javidnia, Nojan Sheybani, Farinaz Koushanfar

This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to reveal the signature and requires re-encoding new ones for code reuse, which potentially compromising the system's usability. To overcome these challenges, RoSeMary obtains high-quality watermarks by training the watermark insertion and extraction modules end-to-end to ensure (i) unaltered watermarked code functionality and (ii) enhanced detectability and robustness leveraging pre-trained CodeT5 as the insertion backbone to enlarge the code syntactic and variable rename transformation search space. In the deployment, RoSeMary uses zero-knowledge proofs for secure verification without revealing the underlying signatures. Extensive evaluations demonstrated RoSeMary achieves high detection accuracy while preserving the code functionality. RoSeMary is also robust against attacks and provides efficient secure watermark verification.

Federico Badaloni, Sebastian Holler, Chrysoula Oikonomou, Pedro Moreno-Sánchez · 5 authors

A smart contract is an interactive program that governs funds in the realm of a single cryptocurrency. Yet, the many existing cryptocurrencies have spurred the design of cross-chain applications that require interactions with multiple cryp-tocurrencies simultaneously. Currently, cross-chain applications are implemented as use-case-specific cryptographic protocols that serve as overlay to synchronize smart contract executions in the different cryptocurrencies. Hence, their design requires substantial expertise, as well as a security analysis in complex cryptographic frameworks. In this work, we present$BitML^{x}$, the first domain-specific language for cross-chain smart contracts, enabling interactions with several users that hold funds across multiple Bitcoin-like cryptocurrencies. We contribute a compiler to automatically translate a$BitML^{x}$contract into one contract per involved cryp-tocurrency and a user strategy that synchronizes the execution of these contracts. We prove that an honest user, who follows the prescribed strategy when interacting with the several contracts, ends up with at least as many funds as in the corresponding execution of the$BitML^{x}$contract. Last, but not least, we implement the$BitML^{x}$compiler and demonstrate its utility in the design of illustrative examples of cross-chain applications such as multi-chain donations or loans across different cryptocurrencies.

Jiaxi Liu, Li Guo, Tianyu Kang

The rapid development of blockchain has significantly promoted research on zero-knowledge proofs (ZKPs), especially zero-knowledge succinct noninteractive arguments of knowledge (zk-SNARK). As is well known, protocol proof and verification time, as well as proof size, are the main obstacles that restrict the implementation of ZKPs in practical applications, so they have become the main concerns of researchers in recent years. This work achieves a new recursive zk-SNARK called GENES, which does not have a trusted setup and is secure under the standard discrete logarithm assumption. GENES is designed from the form of the rank-1 constraint system (R1CS) satisfiability problem. Recursive proof composition is achieved by merging multiple R1CS instances, which transforms the verification of numerous proofs into the verification of a single proof. Moreover, multi-helpers amortize proof commitments in this study, significantly reducing the computational pressure and time cost of proof generation. Compared with previous work, GENES effectively improves the proof time and verification time, but at the cost of larger proof sizes. We provide a blockchain Layer-1 scaling solution leveraging GENES to demonstrate its practicality.

G. Archana, Rajeev Goyal, K. M. V. Madan Kumar

Internet of Things (IoT) is adopted in a wide spectrum of applications in which a vast amount of data are produced and distributed to centralized cloud platforms to deliver various services. It involves smart devices that collect thousands of terabytes of heterogeneous data and deployed this to make instant decision that aids for the better performance and most comfort life. Traditional IoT architecture is heavily centralized, where it stores the most sensitive information that creates the multiple threats and security breaches as the attackers target towards these centralized cloud systems. To improve the security chain in IoT environment, edge computing (EC) was introduced to distribute the applications of IoT at the edge of the communication networks. However, these edge-based IoT are also vulnerable to many threats due to their decentralized and in secured management. Block chain (BC) technology offers a most trusted solution to resolve the security issues in the IoT-Edge computing environment. This research study presents the block chain driven medical image encryption technique using modified honey badger optimization with the ensemble chaotic systems. The proposed block chain framework uses the divergent methods that integrates differential scroll, Hénon chaotic maps and modified honey badger optimization to generate the optimum keys and high secured image data. These high secured data are stored in the block chain, ensuring the image security to be stored in edge nodes. The complete framework was experimented using Ethereum using Ganache API and Python3.19 are utilized as the major programs for designing the varied interfaces of the recommended model. The comprehensive experimentation is undertaken to assess the security strength of the recommended encryption scheme. The evaluation metrics like as NACI, UACI, Entropy and standard verification methods such as NIST standard tests are deployed and analyzed. To prove it security strength, proposed secured BC framework is compared with the wide-variety of secured frameworks. The experimental findings reveal that the suggested framework establishes a more robust and secure environment for image exchange, surpassing the performance of other blockchain-based systems in terms of integrity, robustness and security. Finally, the paper spreads the bright light of advantages in deploying the proposed framework to formulate the most secured environment in the IoT-Edge environment for medical image transmission.

Jianan Liu, Yongjuan Wang, Siqi Lu, Gang Yu · 6 authors

Abstract The rapid growth in the speed and convenience of information dissemination has made copyright infringement increasingly common. Blockchain technology solves pain points such as difficulties in traditional copyright registration, easy infringement, and difficulties in confirming and safeguarding rights. It also realises the decentralised management of copyright, network-wide tracking and monitoring, trusted certificate deposits, among others. However, the efficient original authentication of works and the function of blockchain to create copyright trading channels in the field of copyright are often ignored. This paper designed a self-adaptive learning similarity detection fusion strategy to protect the copyright of original digital works, namely SAAChain, and built a platform for releasing and storing original works based on non-fungible tokens. SAAChain first measures the similarity of a work based on adaptive learning to realise the originality authentication of works. Secondly, the works are stored on the InterPlanetary File System as NFTs, along with copyright information. Finally, a smart contract based on Ethereum and ERC-721 is designed to realise the free circulation of digital rights while simultaneously constructing an efficient and convenient digital rights protection system. Experiments show that the accuracy of the fusion strategy for adaptive work similarity detection can reach above 97%, which meets the requirements of work originality verification. Because of the storage mode of the platform, the system has good performance in terms of response speed and storage efficiency. The entire process provides a full-process and transparent transaction platform for all parties and guarantees the copyright ownership of works as well as the non-tampering and traceability of copyright information.

Rongxin Guo, Shenglong Liao, Jianqing Zhu

Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios. To address these challenges, this paper proposes CrowdBA: A low-cost, quality-driven crowdsourcing architecture. The CrowdBA utilizes the Ethereum public blockchain as the foundational architecture and develops corresponding smart contracts. First, by integrating Ethereum with the InterPlanetary File System (IPFS), storage and computation processes are shifted off-chain, effectively addressing the high costs associated with data storage and computation on public blockchains. Additionally, the CrowdBA introduces a Dynamic Intersection over the union-weighted bounding box fusion (DWBF) algorithm, which assigns dynamic weights based on IoU to infer true bounding boxes, thereby assessing each worker’s annotation quality. Annotation quality then serves as a key criterion for incentive distribution, ensuring fair and appropriate compensation for all contributors. Experimental results demonstrate that the operational costs of each smart contract function remain within reasonable limits; the off-chain storage and computation approach significantly reduces storage and computation expenses, and the DWBF algorithm shows marked improvements in accuracy and robustness over other bounding box fusion methods.

Pooja Anand, Yashwant Singh, Harvinder Singh

In smart applications, streaming IoT data is essential to building trust in sustainable IoT solutions. However, most existing systems for storing and disseminating IoT data streams lack reliability, security, and transparency, primarily due to centralized architectures that create single points of failure. To address these limitations, this research introduces TraVel, a blockchain and transfer learning-based framework for secure IoT data management. TraVel leverages decentralized IPFS storage to handle large data volumes effectively, integrating with a private Ethereum blockchain to enhance data integrity and accessibility. In the proposed scheme, the smart home ([Formula: see text]) data is collected securely and accessed over the BC with a unique hash key generated on the IPFS for all the files. Self-executing Ethereum smart contracts enforce access control and verify data integrity, allowing only validated, non-malicious data to be stored. An adversarial domain adaptation (DA) learning model is employed to detect and filter malicious data before it enters the blockchain. TraVel's performance is evaluated on blockchain parameters, with simulations conducted on REMIX IDE and InterPlanetary File System (IPFS), demonstrating its reliability and scalability for secure IoT data dissemination.

R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam · 9 authors

With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.

Amanul Islam, Nazmus Sakib, Kelei Zhang, Simeon Wuthier · 5 authors

Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) because of the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while in reality it is not using anonymous routing and forwarding its networking directly to the destination peer to reduce the networking overheads. Using profile detection to detect anonymous routing and false claims based on the deterministic features are vulnerable to spoofing, especially in the permissionless cryptocurrency bypassing registration control. We therefore design and build network fingerprinting using the networking behaviors to detect and classify the networking types. We build a network sensor to collect data on an active Bitcoin node connected to the Mainnet and apply supervised machine learning to classify if a peer node is using IP (not anonymous), Tor, or I2P. Our results show that our scheme is effective in accurately detecting the networking types and identifying spoofing attempts through supervised machine learning. Our machine learning model accurately classifies the networking types and detects fake claims of Tor usage with 90% accuracy and false claims of I2P with 87% accuracy in permisionless Bitcoin.

Qiang Huang, Minsheng Tan, Wenlong Tian

The cross-chain identity authentication method based on relay chains provides a promising solution to the issues brought by the centralized notary mechanism. Nonetheless, it continues to encounter numerous challenges regarding data privacy, security, and issues of heterogeneity. For example, there is a concern regarding the protection of identity information during the cross-chain authentication process, and the incompatibility of cryptographic components across different blockchains during cross-chain transactions. We design and propose a cross-chain identity privacy protection method based on relay chains to address these issues. In this method, the decentralized nature of relay chains ensures that the cross-chain authentication process is not subject to subjective manipulation, guaranteeing the authenticity and reliability of the data. Regarding the compatibility issue, we unify the user keys according to the identity manager organization, storing them on the relay chain and eliminating the need for users to configure identical key systems. Additionally, to comply with General Data Protection Regulation (GDPR) principles, we store the user keys from the relay chain in distributed servers using the InterPlanetary File System (IPFS). To address privacy concerns, we enable pseudonym updates based on the user’s public key during cross-chain transactions. This method ensures full compatibility while protecting user privacy. Moreover, we introduce Zero-Knowledge Proof (ZKP) technology, ensuring that audit nodes cannot trace the user’s identity information with malicious intent. Our method offers compatibility while ensuring unlinkability and anonymity through thorough security analysis. More importantly, comparative analysis and experimental results show that our proposed method achieves lower computational cost, reduced storage cost, lower latency, and higher throughput. Therefore, our method demonstrates superior security and performance in cross-chain privacy protection.

Shaik Mulla Almas, K. Kavitha, Kalavathi Alla

Using blockchain technology and smart transportation gadgets, this paper proposes the next-generation VANET system. While VANET has many advantages, it must first be improved in areas such as security and privacy if it is to be widely adopted. Nearby vehicles periodically exchange events providing their unique identifiers, locations, speeds, and statuses. Using key cryptography, it must verify the legitimacy of each car in the network before allowing it to participate, and it must take the blame for any malicious activity that occurs on the road. Due to the fast speeds of vehicles, limited communication capacity, and delay sensitive applications, traditional centralised security solutions are not applicable in VANET. The purpose of this study is to imagine a new blockchain protocol for secure event transactions in a virtual autonomous network (VANET). Blockchain is a distributed ledger system that facilitates resource tracking and administration without the need for a central authority. Therefore, a blockchain-based solution that offers transparency, tamper resistance, and immutability is preferable in a VANET scenario.

Kalash, Bishakh Chandra Ghosh, Sourav Kanti Addya

No abstract is available for this record.

Mohammad Shahab Sepehri, Asal Mehradfar, Mahdi Soltanolkotabi, Salman Avestimehr

Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets. Traditional time-series models, such as ARIMA and GARCH, and recurrent neural networks, like LSTMs, have been widely applied to this task but struggle to capture the regime shifts and long-range dependencies inherent in the data. In this work, we propose CryptoMamba, a novel Mamba-based State Space Model (SSM) architecture designed to effectively capture long-range dependencies in financial time-series data. Our experiments show that CryptoMamba not only provides more accurate predictions but also offers enhanced generalizability across different market conditions, surpassing the limitations of previous models. Coupled with trading algorithms for real-world scenarios, CryptoMamba demonstrates its practical utility by translating accurate forecasts into financial outcomes. Our findings signal a huge advantage for SSMs in stock and cryptocurrency price forecasting tasks.

Aryan Heidari

No abstract is available for this record.

Xin Tan, Xiaoxin Lin, Anxue Yin, Le Wang · 5 authors

No abstract is available for this record.

Ioannis Giannakouros

No abstract is available for this record.

Gyeong Min Baek, Young Ah Shin, Gye Hyun Jang, Ik Rae Jeong

Bitcoin address clustering is a technique used for Bitcoin deanonymization to determine address ownership. It serves as a valuable method for assessing the level of anonymity within the system and detecting addresses associated with illicit activities. The recent Taproot soft fork in Bitcoin has introduced significant privacy enhancements, making deanonymization more challenging. In this paper, we propose a novel on-chain heuristic approach, block number-based address clustering, which leverages the fundamental principle of 6 block confirmations for double-spending prevention. By relying on the trust relationships between transacting parties, our method enables the generation of clusters even upon the soft fork, where existing methods encounter challenges. To evaluate our heuristics, we analyze our approach using the address reduction ratio and pure contribution as key metrics. The results show the effectiveness of our method both before and after the soft fork, which implies that our approach remains unaffected by the usage of Taproot transactions. Furthermore, we provide real-world empirical cases that validate the practicality of our heuristics. As shown in the investigation cases conducted by the Korean National Police Agency, our method can generate new clusters and improve address ownership identification compared to the previous approaches.

Garima Mathur, Pushpraj Singh Chauhan, Privi Dubey

No abstract is available for this record.