Transaction Privacy Research Articles

The Adequacy of the UAE Commercial Law in 2023 in Regulating Artificial Intelligence as a Subject of the Contract

The study aims to identify the adequacy of the UAE Commercial Law in 2023 in regulating artificial intelligence as a subject of the contract, This will be done by searching for the way in which the UAE legislator intervened to control the good and effective use of artificial intelligence, whether it was able to reconcile between protecting the interests of the parties to the transaction and encouraging technological development and investment in artificial intelligence to achieve the goals of the National Strategy for Artificial Intelligence. Then, the cases of using artificial intelligence as a subject for commercial transactions will be highlighted, because artificial intelligence is considered one of the funds and products that can be the subject of commercial transactions between professional traders, which remain subject to the applicable legislation, even those that link the trader to the consumer due to the availability of the elements of the consumer process in this type of transaction, which is concerned with protecting the consumer because the consumer transaction must be carried out with transparency and responsibility. However, as a result of the privacy of such transactions, the current legislation does not appear sufficient, but rather work must be done to establish legislative provisions to control dealings in artificial intelligence at the local and international levels to govern artificial intelligence.

PTTS: Zero-knowledge proof-based private token transfer system on Ethereum blockchain and its network flow based balance range privacy attack analysis

Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analysed. We propose a Private Token Transfer System (PTTS) for the Ethereum public blockchain in the first part of this paper. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. With the help of web user interface designed, the end users can interact with the smart contract without any third-party setup. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack which has been modeled as a network flow problem. It is shown that in case some balance ranges are deliberately leaked out to particular organizations or adversarial entities, it is possible to extract meaningful information about the user balances by employing minimum cost flow network algorithms that have polynomial complexity. The experimental study reports the Ethereum gas consumption and proof generation times for the proposed framework. It also reports network solution times and goodness rates for a subset of addresses under the balance range privacy attack with respect to number of addresses, number of transactions and ratio of leaked transfer transaction amounts.

Duo‐H: An Effectual Consensus Algorithm Using Two‐Tier Shard Consortium Blockchain Mechanism for Enhanced Privacy Protection

ABSTRACTBlockchain is an innovative technology for storing data in decentralized, distributed, and secure chain blocks. Consortium blockchains are commonly used in transactions where transactions between organizations are also achieved by the blockchain. In the classic consortium blockchain system, entire nodes are added to each other in the process of transaction consensus. This leads to lower confidentiality in protecting transaction data within the organizations in the consortium. The throughput of the existing consortium blockchain system is still low. To solve the above problems, the paper proposes a two‐tier consortium blockchain with transaction privacy based on sharding technology. First, a trust value assessment is carried out to select the nodes of the blockchain. The duo‐head observation strategy uses these trust values to identify the nonmalicious node. Finally, the consensus separation approach based on the guarantee mechanism strategy with the shard nodes is presented. This approach is used to select reliable nodes for the blocks to be stored. The proposed consortium blockchain approach evaluation is done in terms of latency, throughput, and transactions per second metrics. As a result of the evaluations, the proposed model with 32 shards possesses 143,891 of throughput and 1.11 s of latency. Moreover, by the proposed two‐tier consortium model, time consumption is also decreased when uploading data. For a data set of 50,000, the suggested model has a time consumption of 96 s. The proposed research results in higher throughput and less latency in transactions. Also, the research enhances the scalability and reliability by overcoming the poor node issues.

PEBASI: A Privacy preserving, Efficient Biometric Authentication Scheme based on Irises

We introduce a novel privacy-preserving biometric authentication scheme based on irises that allows a user to enroll once at a trusted biometric certification authority (BCA) and authenticate to online service providers (SPs) multiple times without involving the BCA during the authentication. Our scheme preserves the user’s biometric privacy from the SPs and transactional privacy from the BCA, while providing security against a malicious user. During the enrollment, the BCA issues a signed token that encrypts the user’s biometrics. We introduce techniques enabling the SP and the user to perform secure computation of biometric matching between such encrypted biometrics and the user’s biometrics captured at the authentication time. We provide a prototype implementation, a performance evaluation, and a security analysis of the protocol.

Research on transaction privacy protection solutions for cross‐border commerce

Research on transaction privacy protection solutions for cross‐border commerce

Counter: A Novel Scheme Ensuring Compliance and Privacy in Cryptocurrency-Based Blockchain

An extremely secure cryptocurrency, commonly known as the Decentralized Anonymous Payment System (DAP), stands as one of the most innovative and widely embraced applications in the field of blockchain. Although the DAP provides a strong degree of transaction privacy and user anonymity, it has been utilized illegally in criminal activities, constituting security challenges to governments and financial institutions. In this paper, we propose a solution known as Counter: A Novel Scheme Ensuring Compliance and Privacy in Cryptocurrency-Based Blockchain, which aims to preserve transaction privacy while providing governments with supervision and enforcing regulations over transactions. Our suggested counter imposed limitations on the transfer of large amounts of cryptocurrencies and also imposed a restriction on the trading of cryptocurrencies during specific time periods. The principal feature of our proposed system involves employing the Order-Preserving Encryption (OPE) and Pedersen Commitment to implement a financial policy ensuring that the transferred amount remains within the allowed range and does not exceed the limit. Besides, we utilized the Unix timestamp to guarantee that the transaction is carried out within the allowed date and time frame. We utilized the RSA accumulator to provide government supervision and enforce regulations. Our suggested counter employs the combination of the ring signature, Pedersen Commitment, and stealth address to protect transaction privacy. Our system fulfills all the security requirements for organizing digital assets that have been defined by international financial institutions and authorities. Finally, the assessment shows that our system is effective and efficient compared to other systems.

Leveraging Cryptographic Hash Functions for Credit Card Fraud Detection

Credit card fraud remains a significant challenge in the financial industry, posing substantial financial losses to both consumers and businesses. Traditional fraud detection methods often rely on rule-based approaches and statistical models, which may struggle to keep pace with evolving fraud tactics and sophisticated cyber threats. In this paper, we propose a novel approach to credit card fraud detection leveraging cryptographic hash functions. Cryptographic hash functions offer robust security guarantees, including collision resistance and preimage resistance, making them well-suited for ensuring the integrity and authenticity of transaction data. Our proposed system employs cryptographic hash functions, such as SHA-256, to generate unique hash values for credit card transactions. These hash values serve as digital fingerprints of the transaction data, enabling secure verification and auditing of transactions on the blockchain. We conducted experiments using a dataset of 100,000 credit card transactions, evaluating the performance of our system in terms of accuracy, precision, recall, and F1-score. The results demonstrate the effectiveness of our approach in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we discuss the implications of our findings and explore future research directions, including the integration of advanced cryptographic techniques and blockchain technology to enhance the security and privacy of credit card transactions. Overall, our study underscores the importance of cryptographic hash functions in building robust and secure fraud detection systems capable of combating emerging fraud threats in the digital era.

Distributed prosumer trading in the electricity and carbon markets considering user utility

With the promotion of distributed energy trading and the transformation of power system operation mode, prosumers in the carbon and electricity markets face new opportunities and challenges. This paper proposes a peer-to-peer transaction platform to promote renewable energy consumption and carbon emission reduction, including multiple types of loads, coal-fired units, and microgrid prosumers. The carbon-emitting members of power generation and demand sides in the electricity market are introduced into the carbon market, and their carbon cost model is established. To guide consumption behavior of demand side, the user utility model based on electricity consumption is built to reduce carbon emissions. With the goal of optimizing the generation cost, user utility, and network fees, the distributed optimal scheduling model is established to recover the network fees. Finally, the alternating direction method of multipliers is used to solve this problem and protect transaction privacy. The results indicate that introducing carbon trading at the power generation and demand sides can promote the shift of members' production behavior from high-carbon emission industries to low-carbon ones. Additionally, an appropriate network fee mechanism and unit fee can guide market members to adjust their trading behavior adaptively and help the power grid recover costs.

Implementing Quantum Resistant Algorithm in Blockchain-Based Applications

With quantum computing evolving very fast as we speak, the security and integrity of blockchain-based applications will become the most crucial aspect. A proposal is raised to use blockchain technology as a platform for writing and probating ‘wills’. Blockchain technology in drafting and probating wills makes them safe from manipulations, highly secure, and transparent. It also dramatically decreases the time required without catering for the challenges created by the current system. [9] This paper presents a new method for will transfer and inheritance management by implementing quantum-resistant algorithms in the security architecture of a blockchain decentralized application (DApp). The system uses IPFS Network for data storage and quantum-safe algorithms as retrieval and sending algorithms. The system includes Quantum-Resistant Dilithium Signatures and Merkle trees as the fundamental components for safeguarding the transfers of assets and claims for inheritance. Quantum-Resistant Dilithium Signatures offer an unbreakable shield against quantum attacks that are expected to happen, which in turn safeguards the privacy and authenticity of transactions. While Merkle trees are responsible for the organization of inheritance claims in an effective and tamper-proof manner, the introduced system incorporates smart contracts to address the execution of an inheritance case, adding more security and automation to the asset distribution process. The system ensures a robust security framework by integrating quantum-resistant algorithms at the very core of the blockchain DApp for instance, retrieval and sending. This research is of great significance to blockchain technology which is the emerging technology of the future because it addresses the existing threat of quantum computing by showing the feasibility of using quantum-resistant algorithms in practical applications. As established by the findings, besides Quantum-Resistant Dilithium Signatures and Merkle trees, the systems of asset transfers and inheritance management within blockchain networks are enhanced in terms of safety and reliability. Hence, paving the road to the creation of more secure and trustworthy digital asset management systems.

Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning

To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.

Leveraging AI and Blockchain for Privacy Preservation and Security in Fog Computing

INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI).
 OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based.
 METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it.
 RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced.
 CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.

FGDB‐MLPP: A fine‐grained data‐sharing scheme with blockchain based on multi‐level privacy protection

AbstractIn the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.

Privacy Protection Method for Blockchain Transactions Based on the Stealth Address and the Note Mechanism

Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution.

Semi-quantum blind double signature scheme with semi-trusted arbitrator

In order to solve the security of electronic transactions on limited goods, some quantum blind double signature (QBDS) protocols were proposed. In existing QBDS protocols, all participants must have specific quantum computing capabilities. Realistically, high cost of quantum devices may hinder people from using the QBDS protocol. Therefore, we propose a semi-quantum arbitrator blind double signature (SQABDS) scheme. In this scheme, the sender (signer) and the receiver both are classical participants, which can relatively reduce the dependence on quantum hardware. The introduction of blind signers can protect the privacy of electronic transactions, the introduction of semi-trusted arbitrator can relax the strong assumption about the trustworthiness of the arbitrator, for the arbitrator cannot forge the signature. In addition, the SQABDS scheme can be used to sign classical messages directly without performing any quantum swapping tests or quantum gate operations. Finally, the analysis shows that our scheme is securer and its quantum cost is relatively lower.

Research on the method to enhance the transparency of financial transactions by integrating blockchain and smart contracts

Abstract Financial transaction transparency has gradually become one of the main directions for the development and construction of the financial transaction market. This paper integrates blockchain and smart contracts and proposes a strategy to improve financial transaction transparency in order to protect transaction data privacy and identify and trace transaction anomalies. The proposed DM-IBBE scheme for smart contract transaction privacy involves choosing different interpolation points based on Lagrange interpolation curves and creating encryption modes that meet the requirements for financial transaction privacy. Based on a graph neural network, the propagation probability of abnormal transactions is calculated from the blockchain network topology using the TAGCN model, and the influence of irrelevant noise pairs is eliminated to realize the identification and traceability of abnormal transactions. Taking the financial transaction platform of City A as the research object and carrying out the practice of financial transaction optimization, the evaluation scores of the first-level indexes of comprehensive government transparency, transaction process transparency, operation result transparency, process service transparency, and operation transparency and guarantee are 76.58, 88.93, 95.42, 89.51, and 88.43, and except for the indexes of comprehensive government transparency, the other indexes are all greater than 80 points. The second-level indicators’ evaluation value increases from the 50–70 score range before financial transactions optimization to the 80–100 score range. The financial transaction platform in City A has significantly improved the transparency of financial transactions.

RSHealth: A Ring Signature Scheme for Identity Anonymization and Transaction Privacy in Blockchain Based E-Healthcare Systems

RSHealth: A Ring Signature Scheme for Identity Anonymization and Transaction Privacy in Blockchain Based E-Healthcare Systems

A User Transaction Privacy Protection Protocol Supporting Regulations on Account Based Blockchain

A User Transaction Privacy Protection Protocol Supporting Regulations on Account Based Blockchain

SGXonerate:Finding (and Partially Fixing) Privacy Flaws in TEE-based Smart Contract Platforms Without Breaking the TEE

TEE-based smart contracts are an emerging blockchain architecture, offering fully programmable privacy with better performance than alternatives like secure multiparty computation. They can also support compatibility with existing smart contract languages, such that existing (plaintext) applications can be readily ported, picking up privacy enhancements automatically. While previous analysis of TEE-based smart contracts have focused on failures of TEE itself, we asked whether other aspects might be understudied. We focused on state consistency, a concern area highlighted by Li et al., as well as new concerns including access pattern leakage and software upgrade mechanisms. We carried out a code review of a cohort of four TEE-based smart contract platforms. These include Secret Network, the first to market with in-use applications, as well as Oasis, Phala, and Obscuro, which have at least released public test networks. The first and most broadly applicable result is that access pattern leakage occurs when handling persistent contract storage. On Secret Network, its fine-grained access pattern is catastrophic for the transaction privacy of SNIP-20 tokens. If ERC-20 tokens were naively ported to Oasis they would be similarly vulnerable; the others in the cohort leak coarse-grained information at approximately the page level (4 kilobytes). Improving and characterizing this will require adopting techniques from ORAMs or encrypted databases. Second, the importance of state consistency has been underappreciated, in part because exploiting such vulnerabilities is thought to be impractical. We show they are fully practical by building a proof-of-concept tool that breaks all advertised privacy properties of SNIP-20 tokens, able to query the balance of individual accounts and the token amount of each transfer. We additionally demonstrate MEV attacks against the Sienna Swap application. As a final consequence of lacking state consistency, the developers have inadvertently introduced a decryption backdoor through their software upgrade process. We have helped the Secret developers mitigate this through a coordinated vulnerability disclosure, after which their state consistency should be roughly on par with the rest.

Research on transparent management of economic transactions using blockchain technology

Abstract The data asset trading market is gradually emerging with the development of the digital economy, making the development of a safe and effective data asset trading management system an important research direction. This paper uses blockchain technology to create a transparent management system for economic transactions. A PHRP algorithm is proposed to guarantee the security of the transaction, addressing the privacy protection issue involved in the transaction process. The PHRP algorithm calculates the actual transaction amount by encrypting both the input and output amounts, utilizing the additive homomorphic property. It also ensures the undeniability and integrity of the transaction by encrypting and committing random numbers. The relevant test results show that the transaction privacy protection scheme based on the PHRP algorithm can better meet the requirements of the economic transaction management system. Users’ satisfaction with the system constructed in this paper in terms of perceived usefulness and perceived ease of use scored high on average, 4.23 and 4.17, respectively, and expressed average for security, for which this paper proposes relevant improvement strategies.

PDHSMICK: A partial key-based secure EHRs with distributed cloud for healthcare systems applying MI-CRYSTALS-Kyber

Healthcare, which raises several challenges regarding security and privacy, is the largest sector of society dealing with patients’ health data. Numerous approaches have been developed regarding healthcare; however, these approaches lead to high response time and leakage of transaction privacy. Thus, this work proposes PDHSMICK, a partial key-based secure EHRs with distributed cloud for Healthcare Systems applying Mimetic Interpolation-based CRYSTALS-Kyber (MI-CRYSTAL-Kyber). Primarily, the patient and doctor register with the system. After that, the patient logs in and books an appointment with the doctor. Then, consultation is done, and their details are sent to the Third-Party Auditor (TPA), where they store the information in the log file. Afterwards, the patient verifies the log file and then uploads the information to the concerned Cloud Server (CS). During uploading, the files are split, and the attributes from the files and Distributed Cloud Server (DCS) are extracted. Then, by utilizing Interpolation-based Pearson Correlation Coefficient (I-PCC), a particular DCS is selected. After that, for authorization purposes, a hash code and digital signature are generated by utilizing Exponential based Extensible Output Functions (EX-EOF) and Double Mod-Digital Signature Algorithm (DM-DSA). Concurrently, the files are encrypted by employing MI-CRYSTAL-Kyber techniques. Lastly, the doctor logs on to the system, and hash code and digital signature verification are performed. The system allows the doctor to download the data if the verification matches. Also, the experimental assessment was validated, which shows the proposed technique’s efficacy.