In the domain of spatial crowdsourcing drone services, which includes tasks like delivery, surveillance, and data collection, secure communication is paramount. The Public Key Infrastructure (PKI) ensures this by providing a system for digital certificates that authenticate the identities of entities involved, securing data and command transmissions between drones and their operators. However, the centralized trust model of traditional PKI, dependent on Certificate Authorities (CAs), presents a vulnerability due to its single point of failure, risking security breaches. To counteract this, the paper presents D2XChain, a blockchain-based PKI framework designed for the Internet of Drone Things (IoDT). By decentralizing the CA infrastructure, D2XChain eliminates this single point of failure, thereby enhancing the security and reliability of drone communications. Fully compatible with the X.509 standard, it integrates seamlessly with existing PKI systems, supporting all key operations such as certificate registration, validation, verification, and revocation in a distributed manner. This innovative approach not only strengthens the defense of drone services against various security threats but also showcases its practical application through deployment on a private Ethereum testbed, representing a significant advancement in addressing the unique security challenges of drone-based services and ensuring their trustworthy operation in critical tasks.
Blockchain technology is a key technology in the current information field and has been widely used in various industries. Blockchain technology faces significant challenges in privacy protection while ensuring data immutability and transparency, so it is crucial to implement private computing in blockchain. To target the privacy issues in blockchain, we design a secure multi-party computation (SMPC) protocol DHSMPC based on homomorphic encryption in this paper. On the one hand, homomorphic encryption technology can directly operate on ciphertext, solving the privacy problem in the blockchain. On the other hand, this paper designs the directed decryption function of DHSMPC to resist malicious opponents in the CRS model, so that authorized users who do not participate in the calculation can also access the decryption results of secure multi-party computation. Analytical and experimental results show that DHSMPC has smaller ciphertext size and stronger performance than existing SMPC protocols. The protocol makes it possible to implement complex calculations in multi-party scenarios and is proven to be resistant to various semi-malicious attacks, ensuring data security and privacy. Finally, this article combines the designed DHSMPC protocol with blockchain and cloud computing, showing how to use this solution to achieve trusted data management in specific scenarios.
Data owners seeking to boost processing power, storage, or bandwidth can take advantage of cloud computing services. However, this shift poses new challenges related to privacy and data security. Searchable Encryption (SE), which combines encryption and search techniques, addresses these issues (violation of data users' privacy) by allowing user data to be encrypted, transmitted to a cloud server, and searched using keywords. Despite its benefits, several recent real-world attacks have raised concerns about the security of searchable encryption. Ensuring forward and backward privacy is likely to become a standard requirement in the development of new SE systems. To address these issues, we propose a scheme that exclusively uses symmetric cryptographic primitives, achieving high communication efficiency and forward and backward privacy. In addition, we emphasize improved I/O efficiency because only the results of subsequent updates are loaded when searching. The time required to retrieve results is so significantly reduced compared to existing SE methods that we have shown that our scheme achieves superior efficiency. Moreover, by integrating blockchain network services with cloud services, we have developed a searchable intelligent cryptosystem suitable for lightweight smart devices. In our study conducted on the Ethereum network, we found our method to be both efficient and secure, especially when compared to methods such as PPSE and Jiang. The results indicate that our system delivers results in terms of performance and privacy within dynamic cloud environments making it a solution for protecting confidential information.
Na Wang, Yao Zhao, Youyang Qu, Lei Cui · 6 authors
Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.
The use of digital certificates is crucial for verifying the authenticity of various credentials in our digital age.However, traditional digital certificate systems suffer from centralization, vulnerability to tampering, and the risk of loss.This paper presents an approach to issuing and managing certificates as Non-Fungible Tokens (NFTs) on the Ethereum blockchain, ensuring their immutability and perpetual existence.The proposed system aims to overcome the limitations of traditional methods by utilizing decentralized storage through the InterPlanetary File System (IPFS) and implementing a robust incentive mechanism for voting and Proof of Stake; the goal is to achieve true permanence and enhanced security for digital certificates.A first phase of a smart contract and front-end interface was achieved, to preserve diplomas and provide a reliable method for verifying their authenticity for employers and other stakeholders.This paper is part of an ongoing effort to develop a robust system, create a prototype to validate the perpetuity assumption, and propose improvements for a global, enduring repository of certificates.
Biometric authentication has been used in applications in various environments as a secure authentication method in computing systems.When combined with blockchain technology, the security of the biometric authentication system can be further enhanced.In this paper, we propose a biometric authentication system that does not expose the original biometric information during the user's biometric authentication process by utilizing a fully homomorphic encryption.In addition, our proposed authentication system utilizes Ethereum's smart contract, which is one of the most famous public blockchains, to record the authentication log between the user and the service provider in a distributed ledger to enhance accountability and traceability.The system is designed to be used only after obtaining the consent of the biometric data subject(user) to comply with the privacy law represented by the European General Data Protection Regulation (GDPR).Finally, we show that the proposed system can process biometric information while maintaining confidentiality, integrity, and accountability of users via security analysis.The cost of maintaining the proposed system is acceptable by analyzing computation time and blockchain maintenance cost.
Rihab Saidi, Ines Rahmany, Salah Dhahri, Tarek Moulahi
This study presents a novel approach for the early diagnosis of prevalent chest diseases, including COVID-19, pneumonia, and lung cancer, utilizing advanced machine learning techniques. The research focuses on addressing the limitations of traditional diagnostic methods by introducing Federated Learning as a collaborative and privacy-preserving solution. By leveraging Federated Learning, stakeholders can collectively develop accurate diagnostic models without directly sharing sensitive medical data, ensuring both privacy and diagnostic accuracy. Furthermore, the study proposes a multi-classification Federated Learning method enhanced by blockchain technology to reinforce data security and privacy. Experimental results demonstrate the effectiveness of this approach compared to centralized models, showcasing comparable performance in terms of accuracy and superior achievement in terms of privacy preservation. The integration of blockchain into the Federated Learning framework holds promise for a robust system prioritizing data privacy and security in the healthcare domain. This innovative combination not only advances machine learning in medical diagnostics but also sets a forward-looking approach for safeguarding patient information in today’s data-driven healthcare landscape.
Gulshan Kumar, Rahul Saha, Manish Gupta, Tai-hoon Kim
The correctness and the true validated data in Human Resource Management (HRM) are important for organizations as the data plays an impactful role in recruiting, developing, and retaining a skilled workforce. On one hand, the validated data in an organization helps in recruiting legitimate skillful employees; on the other hand, keeping the employee's data safe and maintaining privacy laws such as compliance with the General Data Protection Regulation (GDPR) is also an organization's responsibility. Besides, transparency in human resource management operations is crucial because it promotes trust and fairness within an organization. The present HRM systems are centralized in nature and their verifiable credential system is ineffective; this leads to the intentions of internal data sabotage or internal threats. Besides, the organizations' biases also become more prominent. In this paper, we address the above-mentioned problems with a blockchain framework for HRM to utilize the privacy of data access through a Privacy Information Retrieval (PIR) process. To be specific, our proposed framework called Blockchained piR of resOurces as humaN (BRON) , is the first blockchain framework to show an effective mechanism to access data from organizations globally without hampering privacy. BRON uses a generalized user registration process to use the services of data access and in the background, it uses Zero-Knowledge Proofs (ZKPs) for global verification and PIR for privacy-based data retrieval. More specifically, credential verification and ZKP-based PIR are the highlights of our proposed BRON. Another interesting aspect of BRON is the use of Proof-of-Authority (PoA) to validate the anonymity and unlinkability of any HR operation. Finally, BRON has also contributed with a smart contract to incentivize the employees. BRON is very generic and easily be customizable as per the HR requirements. We run a set of experiments on BRON and observe that it is successful in providing privacy-assured data access and decentralized human resource data management. Overall, BRON provides 30% reduced latency and 35% better throughput as compared to the existing blockchain solutions in the direction of HRM.
Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti, Rafidha Rehiman K. A. · 7 authors
The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Organizations are inadvertently enlarging their vulnerability to cyber threats by integrating more interconnected devices into their operations, which makes these threats both more sophisticated and more common. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to proprietary and confidential data leaks. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality and the trustworthiness of all the participants leveraging some validators through Zero Knowledge Proofs. Extensive experimentation has confirmed the accuracy and performance of our framework. Furthermore, our detailed attack model analyzes its resistance to attacks that could impact data and model quality. • Definition of a Swarm Learning approach for collaborative CTI. • Definition of a Blockchain-based solution for privacy preservation in CTI sharing. • Secure CTI validation using a consensus mechanism and Zero-Knowledge Proof.
Federated learning (FL) represents an advanced approach to tackling the issues linked with training machine learning (ML) models using distributed data while upholding privacy and security. It functions by enabling collaborative model training across a network of edge devices or servers, all without the need to transfer raw data. In place of sending data to a central server, which could potentially compromise privacy, federated learning empowers individual devices to conduct local training on their respective data. These updates are subsequently combined to develop an enhanced global model over multiple iteration. Additionally, as artificial intelligence (AI) becomes pervasive in novel application areas, concerns about the privacy of data and users are on the rise. This article offers an in-depth analysis of the advancements in FL, covering a wide array of topics including methodologies, applications, and challenges. By sidestepping the need to transfer raw data and instead focusing on sharing model updates or gradients, FL ensures the preservation of privacy and the efficient utilization of resources. Additionally, we investigate the diverse spectrum of application domains where FL holds significance. Instances encompass healthcare, finance, agriculture, education, Internet of Things (IoT), and industrial processes, all benefiting from the capacity of federated learning to harness data from decentralized sources without compromising data security. This article addresses complications such as model diversity, Non-IID (independent and identically distributed) data distribution, communication complexities, and security vulnerabilities. Furthermore, we discuss considerations related to regulatory compliance and ethics within the context of federated learning, particularly as data privacy regulations intensify.
Open access
Privacy-Preserving Technologies in Data
Privacy, Security, and Data Protection
Artificial Intelligence in Healthcare and Education
Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficiency issues of BFL in order to guide the researchers and the designers of FL based solution to adopt the most appropriate of each application approach.
This research paper delves into the imperative domain of bolstering data confidentiality within smart contracts through the integration of advanced privacy-preserving methodologies. Smart contracts, pivotal components of blockchain technology, execute self-executing contracts with predefined conditions and are increasingly utilized across various sectors, necessitating stringent data protection measures. The paper addresses the pressing need for fortified data privacy within smart contracts and investigates cutting-edge approaches to mitigate privacy challenges. Two focal techniques under scrutiny are zero-knowledge proofs (SBÇs) and homomorphic encryption. SBÇs facilitate the validation of computations without revealing sensitive data, enabling parties to verify transaction authenticity without disclosing the underlying information. Meanwhile, homomorphic encryption permits computations on encrypted data, preserving confidentiality by allowing operations on encrypted information without the need for decryption. By analyzing these advanced privacy-preserving techniques, this study aims to address the vulnerabilities in data confidentiality present in smart contracts. Its findings hold significant promise in fortifying the security and confidentiality of transactions, thus contributing substantially to the evolution of secure blockchain technology. This research underscores the pivotal role of innovative privacy-enhancing mechanisms in safeguarding sensitive data within smart contracts, ensuring the trust and integrity essential for their widespread adoption.
The fundamental theorem of Goldreich, Micali, and Wigderson (J. ACM 1991) shows that the existence of a one-way function is sufficient for constructing computational zero knowledge (CZK) proofs for all languages in NP. We prove its converse, thereby establishing characterizations of one-way functions based on the worst-case complexities of zero knowledge. Specifically, we prove that the following are equivalent: - A one-way function exists. - NP ⊆ CZK and NP is hard in the worst case. - CZK is hard in the worst case and the problem GapMCSP of approximating circuit complexity is in CZK. The characterization above also holds for statistical and computational zero-knowledge argument systems. We further extend this characterization to a proof system with knowledge complexity O(logn). In particular, we show that the existence of a one-way function is characterized by the worst-case hardness of CZK if GapMCSP has a proof system with knowledge complexity O(logn). We complement this result by showing that NP admits an interactive proof system with knowledge complexity ω(logn) under the existence of an exponentially hard auxiliary-input one-way function (which is a weaker primitive than an exponentially hard one-way function). We also characterize the existence of a robustly-often nonuniformly computable one-way function by the nondeterministic hardness of CZK under the weak assumption that PSPACE ⊈AM. We present two applications of our results. First, we simplify the proof of the recent characterization of a one-way function by NP-hardness of a meta-computational problem and the worst-case hardness of NP given by Hirahara (STOC’23). Second, we show that if NP has a laconic zero-knowledge argument system, then there exists a public-key encryption scheme whose security can be based on the worst-case hardness of NP. This improves previous results which assume the existence of an indistinguishable obfuscation.
Nir Bitansky, Chethan Kamath, Omer Paneth, Ron D. Rothblum · 5 authors
Batch proofs are proof systems that convince a verifier that x1,…,xt ∈ L, for some NP language L, with communication that is much shorter than sending the t witnesses. In the case of statistical soundness (where the cheating prover is unbounded but the honest prover is efficient given the witnesses), interactive batch proofs are known for UP, the class of unique-witness NP languages. In the case of computational soundness (where both honest and dishonest provers are efficient), non-interactive solutions are now known for all of NP, assuming standard lattice or group assumptions. We exhibit the first negative results regarding the existence of batch proofs and arguments: - Statistically sound batch proofs for L imply that L has a statistically witness indistinguishable (SWI) proof, with inverse polynomial SWI error, and a non-uniform honest prover. The implication is unconditional for obtaining honest-verifier SWI or for obtaining full-fledged SWI from public-coin protocols, whereas for private-coin protocols full-fledged SWI is obtained assuming one-way functions. This poses a barrier for achieving batch proofs beyond UP (where witness indistinguishability is trivial). In particular, assuming that NP does not have SWI proofs, batch proofs for all of NP do not exist. - Computationally sound batch proofs (a.k.a batch arguments or BARGs) for NP, together with one-way functions, imply statistical zero-knowledge (SZK) arguments for NP with roughly the same number of rounds, an inverse polynomial zero-knowledge error, and non-uniform honest prover. Thus, constant-round interactive BARGs from one-way functions would yield constant-round SZK arguments from one-way functions. This would be surprising as SZK arguments are currently only known assuming constant-round statistically-hiding commitments. We further prove new positive implications of non-interactive batch arguments to non-interactive zero knowledge arguments (with explicit uniform prover and verifier): - Non-interactive BARGs for NP, together with one-way functions, imply non-interactive computational zero-knowledge arguments for NP. Assuming also dual-mode commitments, the zero knowledge can be made statistical. Both our negative and positive results stem from a new framework showing how to transform a batch protocol for a language L into an SWI protocol for L.
We put forward a new approach for achieving non-interactive zero-knowledge proofs (NIKZs) from the learning with errors (LWE) assumption (with subexponential modulus to noise ratio). We provide a LWE-based construction of a hidden bits generator that gives rise to a NIZK via the celebrated hidden bits paradigm. A notable feature of our construction is its simplicity. Our construction employs lattice trapdoors, but beyond that uses only simple operations. Unlike prior solutions, we do not rely on a correlation intractability argument nor do we utilize fully homomorphic encryption techniques. Our solution provides a new methodology that adds to the diversity of techniques for solving this fundamental problem.
This study examines the issues of privacy protection, data security, and query efficiency in blockchain-based electronic medical record (EMR) sharing. It proposes a secure storage and sharing scheme for EMR based on Hyperledger Fabric and the InterPlanetary File System (IPFS). To mitigate the privacy risks of data mining that could reveal patient identities, we establish an attribution channel in Hyperledger Fabric to store EMR ownership information and a data channel to store the storage location, digest, and usage records of medical data. Encrypted medical data are stored in the IPFS. To improve query efficiency in the blockchain, we integrate queryable medical data attributes into a composite key for conditional queries, avoiding complex data filtering processes. Additionally, we use a zero-knowledge proof combined with smart contracts for decentralized identity verification, eliminating reliance on third-party centralized verification services and enhancing system security. We also integrate AES and proxy re-encryption techniques to ensure data security during sharing. This scheme provides a more secure, efficient, and privacy-preserving approach for EMR systems, with significant practical implications and broad application potential.
William Boitier, Antonella Del Pozzo, Álvaro García-Pérez, Stéphane Gazut · 12 authors
Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper, we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art.
In order to achieve zero-knowledge proof (ZKP) in distributed mobile scenarios, we propose a two-stage multi-prover ZKP framework. Our method utilizes secure multi-party computation (MPC), which has advantages such as flexible adaptation, stable performance, and fewer restrictions compared to existing solutions. In addition, based on the properties of cyclic groups, we optimize secure multi-party summation, improving the balance between security and efficiency, as well as transferability of the algorithm.
Federated learning allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional federated learning is vulnerable to poisoning attacks, which can not only decrease the model performance, but also implant malicious backdoors. In addition, direct submission of local model parameters can also lead to the privacy leakage of the training dataset. In this paper, we aim to build a privacy-preserving and Byzantine-robust federated learning scheme to provide an environment with no vandalism (NoV) against attacks from malicious participants. Specifically, we construct a model filter for poisoned local models, protecting the global model from data and model poisoning attacks. This model filter combines zero-knowledge proofs to provide further privacy protection. Then, we adopt secret sharing to provide verifiable secure aggregation, removing malicious clients that disrupting the aggregation process. Our formal analysis proves that NoV can protect data privacy and weed out Byzantine attackers. Our experiments illustrate that NoV can effectively address data and model poisoning attacks, including PGD, and outperforms other related schemes.
Tahani Baabdullah, Amani Alzahrani, Danda B. Rawat, Chunmei Liu
Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.
Huong Q. Nguyen, Tri Nguyen, Lauri Lovén, Susanna Pirttikangas
This paper presents a fully coupled blockchain-assisted federated learning architecture that effectively eliminates single points of failure by decentralizing both the training and aggregation tasks across all participants. Our proposed system offers a high degree of flexibility, allowing participants to select shared models and customize the aggregation for local needs, thereby optimizing system performance, including accurate inference results. Notably, the integration of blockchain technology in our work is to promote a trustless environment, ensuring transparency and non-repudiation among participants when abnormalities are detected. To validate the effectiveness, we conducted real-world federated learning deployments on a private Ethereum platform, using two different models, ranging from simple to complex neural networks. The experimental results indicate comparable inference accuracy between centralized and decentralized federated learning settings. Furthermore, our findings indicate that asynchronous aggregation is a feasible option for simple learning models. However, complex learning models require greater training model involvement in the aggregation to achieve high model quality, instead of asynchronous aggregation. With the implementation of asynchronous aggregation and the flexibility to select models, participants anticipate decreased aggregation time in each communication round, while experiencing minimal accuracy trade-off.
Anuj Nepal, Mohamed Ahzam Amanullah, Robin Doss, Frank Jiang
The evolution of the Internet of Vehicles (IoVs) presents many opportunities for intelligent transport systems; however, it brings significant security challenges that threaten the security and reliability of the data. Security, reliability, and trustworthiness are the essential critical requirements for the IoVs to ensure secure and efficient decision-making processes in an accurate, secure, and trustworthy manner. Traditional research paradigms have predominantly focused on data integrity, overlooking the essential need for data to be realistic, consistent, trustworthy, and reliable. Addressing this gap, our paper introduces a decentralized secure data provenance (SDP) protocol for the dynamic and distributed nature of IoVs to ensure verifiable security properties regarding data plausibility, source identity, data privacy, location authenticity, and data integrity, properties that are fundamental for achieving SDP. Our protocol integrates Verifiable Credentials (VCs) with distributed ledger technology (DLT), Road-Side Unit (RSU) infrastructure, cryptographic techniques, and plausibility checks to ensure secure, traceable, and tamper-evident data lineage. This comprehensive approach enables the timely identification and mitigation of potential security breaches, such as impersonation, data tampering, and privacy violations which are crucial for maintaining SDP and ensuring data credibility through anomaly detection and plausibility checks. Through detailed security analysis and validation, our methodology demonstrates improved resilience against common threats, ensuring that only credible and verifiable data inform its decision-making processes.
The public blockchain lacks data confidentiality. Although a level of anonymity seems guaranteed, it is still possible to link transactions and disclose related information. A solution to the privacy problem is to use cryptography in transactions, however this can lead to increased costs and slowdown in network throughput. Recent works experiment with advanced cryptography, in particular Zero-Knowledge proofs (ZK-proofs) can be supplied within a transaction to prove its validity, without revealing sensitive information. We analyze solutions that adopt ZK-proofs, such as Confidential Transactions (CTs). Several challenges emerge depending on both the zero-knowledge system and the balance model considered (UTXO, hybrid or account model). For ZK-proofs, systems that do not introduce additional trust are required. On the other hand, the account model is the most flexible for addressing security challenges. Moreover, CTs do not fully exploit the potential of ZK-proofs, since each transaction comes with one or more ZK-proof for a single transfer. Within this paper, we present ZeroMT, a novel multi-transfer private payment scheme for account-based blockchains. Drawing inspiration from Zether, our approach extends their work to develop a payment model that supports multiple payees within a single transaction. This also benefits scalability: ZeroMT enriches the CTs with the aggregation property, i.e., the batch verification of multiple transfers from a single and aggregate proof. We show that in our extended model the overdraft-safety and privacy security properties still hold. We provide an implementation and evaluation of ZeroMT, which shows the benefits of aggregating multiple transfers.