The Borda Count method, a widely used ranked voting system, is known for its fairness and simplicity. However, when applied to large-scale voting systems, it faces challenges related to computational complexity, scalability, and system reliability. This paper proposes an optimization model for the Borda Count method by integrating blockchain consensus mechanisms, including Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT), aiming to enhance the voting process's efficiency, accuracy, and fault tolerance.We explore how blockchain technology can address the computational challenges of Borda Count, ensuring secure, transparent, and decentralized voting while maintaining high system reliability. By leveraging blockchain's immutability and consensus mechanisms, the proposed model significantly reduces computational overhead, increases the robustness of the system against node failures, and improves the accuracy of the voting results.This paper presents an in-depth analysis of the Borda Count method and blockchain consensus mechanisms, outlines a novel optimization algorithm, and provides a theoretical evaluation of the model's performance. We conclude by discussing the advantages of integrating blockchain with Borda Count for distributed voting systems and suggest potential directions for future research.
Stefan A. Iordache, Catalina Camelia Patilea, Ciprian Păduraru
In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain’s decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles.
Yalan Wang, Liqun Chen, Long Meng, Christopher J. P. Newton
Concerns about how third parties manage personal information have led to the development of decentralized identities (DIDs) and verifiable credentials (VCs). The World Wide Web Consortium (W3C) working group has been developing standards for DIDs and VCs. In the W3C standards, a DID identifies an entity (a DID holder) and a VC confirms that this DID holder has some associated attributes. A DID holder can obtain many VCs and confirm any number of these VCs to others (verifiers) in verifiable presentations (VPs). In order to keep a holder’s identity and attributes private, it is necessary to achieve anonymous VPs that allows this information to be kept confidential. The W3C working group recommends using randomizable signatures to create VCs with zero-knowledge proofs for this purpose. However, the anonymous VPs provided by the this method are limited that in the real world, credentials in cross domains cannot be universally verified. To overcome this limitation, in this paper, we propose a new scheme, called Verifiable Credentials with anonymous DIDs (VCaDID), which aims to achieve anonymous VPs in cross-domain settings. The main technique in our VCaDID scheme is a ring signature with multiple attributes by hiding a holder’s public key among a ring of holders. In our scheme, we set private keys associated with the holder’s DID and attributes, which allow the holder to anonymously present these credentials in a verifiable way. We also prove that the proposed VCaDID scheme satisfies correctness, anonymity and unforgeability under security assumptions of discrete log and random oracle model. Finally, we implement our scheme to demonstrate its feasibility.
Antonio Jesús Chaves, Cristian Martín, Kwang Soon Kim, Adnan Shahid · 5 authors
Machine learning data privacy has been improved with Federated Learning approaches. However, some obstacles to guaranteeing traceability, openness, and participant contribution incentives prevent its widespread use. In this study, Ethereum blockchain technology is integrated into the data stream Kafka-ML framework, presenting a novel asynchronous and blockchain-based Federated Learning approach. By utilising Ethereum for transparent and auditable participant tracking, this integration overcomes some shortcomings such as auditability and model sharing reliability. Furthermore, Ethereum smart contracts allow for automatic reward distribution systems, which promote equitable incentive systems and increased involvement in the Federated Learning process. To demonstrate its potential, an extensive evaluation has been carried out on a wireless net-work technology detection use case. By improving transparency, traceability, and incentive structures of Federated Learning, it is expected to strengthen the robustness of flexible machine learning collaboration with data streams.
Dongliang Cai, Liang Zhang, Borui Chen, Haibin Kan
Decentralized data sovereignty and secure data exchange are regarded as foundational pillars of the new era. Attribute-based encryption (ABE) is a promising solution that enables fine-grained access control in data sharing. Recently, Hohenberger et al. (Eurocrypt 2023) introduced registered ABE (RABE) to eliminate trusted authority and gain decentralization. Users generate their own public and secret keys and then register their keys and attributes with a transparent key curator. However, RABE still suffers from heavy decryption overhead. A natural approach to address this issue is to outsource decryption to a decryption cloud server (DCS). In this work, we propose the first auditable RABE scheme with reliable outsourced decryption (ORABE) based on blockchain. First, we achieve verifiability of transform ciphertext via a verifiable tag mechanism. Then, the exemptibility, which ensures that the DCS escapes false accusations, is guaranteed by zero knowledge fraud proof under the optimistic assumption. Additionally, our system achieves fairness and auditability to protect the interests of all parties through blockchain. Finally, we give concrete security and theoretical analysis and evaluate our scheme on Ethereum to demonstrate feasibility and efficiency.
Data sharing is ubiquitous in the metaverse, which adopts blockchain as its foundation. Blockchain is employed because it enables data transparency, achieves tamper resistance, and supports smart contracts. However, securely sharing data based on blockchain necessitates further consideration. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to provide confidentiality and fine-grained access control. Nonetheless, authority accountability and key abuse are critical issues that practical applications must address. Few studies have considered CP-ABE key confidentiality and authority accountability simultaneously. To our knowledge, we are the first to fill this gap by integrating non-interactive zero-knowledge (NIZK) proofs into CP-ABE keys and outsourcing the verification process to a smart contract. To meet the decentralization requirement, we incorporate a decentralized CP-ABE scheme into the proposed data sharing system. Additionally, we provide an implementation based on smart contract to determine whether an access control policy is satisfied by a set of CP-ABE keys. We also introduce an open incentive mechanism to encourage honest participation in data sharing. Hence, the key abuse issue is resolved through the NIZK proof and the incentive mechanism. We provide a theoretical analysis and conduct comprehensive experiments to demonstrate the feasibility and efficiency of the data sharing system. Based on the proposed accountable approach, we further illustrate an application in GameFi, where players can play to earn or contribute to an accountable DAO, fostering a thriving metaverse ecosystem.
Ensuring security for highly dynamic peer-to-peer (P2P) networks has always been a challenge, especially for services like online transactions and smart devices. These networks experience high churn rates, making it difficult to maintain appropriate access control. Traditional systems, particularly Role-Based Access Control (RBAC), often fail to meet the needs of a P2P environment. This paper presents a blockchain-based access control framework that uses Ethereum smart contracts to address these challenges. Our framework aims to close the gaps in existing access control systems by providing flexible, transparent, and decentralized security solutions. The proposed framework includes access control contracts (ACC) that manage access based on static and dynamic policies, a Judge Contract (JC) to handle misbehavior, and a Register Contract (RC) to record and manage the interactions between ACCs and JC. The security model combines impact and severity-based threat assessments using the CIA (Confidentiality, Integrity, Availability) and STRIDE principles, ensuring responses are tailored to different threat levels. This system not only stabilizes the fundamental issues of peer membership but also offers a scalable solution, particularly valuable in areas such as the Internet of Things (IoT) and Web 3.0 technologies.
A privacy pool enables clients to deposit units of a cryptocurrency into a shared pool where ownership of deposited currency is tracked via a system of cryptographically hidden records. Clients may later withdraw from the pool without linkage to previous deposits. Some privacy pools also support hidden transfer of currency ownership within the pool. In August 2022, the U.S. Department of Treasury sanctioned Tornado Cash, the largest Ethereum privacy pool, on the premise that it enables illicit actors to hide the origin of funds, citing its usage by the DPRK-sponsored Lazarus Group to launder over $455 million dollars worth of stolen cryptocurrency. This ruling effectively made it illegal for U.S. persons/institutions to use or accept funds that went through Tornado Cash, sparking a global debate among privacy rights activists and lawmakers. Against this backdrop, we present Derecho, a system that institutions could use to request cryptographic attestations of fund origins rather than naively rejecting all funds coming from privacy pools. Derecho is a novel application of proof-carrying data, which allows users to propagate allowlist membership proofs through a privacy pool's transaction graph. Derecho is backwards-compatible with existing Ethereum privacy pool designs, adds no overhead in gas costs, and costs users only a few seconds to produce attestations.
The rise of Autonomous Vehicles (AVs) brings with it the need for secure and privacy-preserving machine learning models. Federated Learning (FL) allows AVs to collaboratively train models while keeping raw data localized. However, traditional FL systems are vulnerable to security threats, including adversarial attacks, data breaches, and dependency on a central aggregator, which can be a single point of failure. To address these concerns, this paper introduces a peer-to-peer decentralized federated learning system that integrates lightweight blockchain technology and Binius Zero- Knowledge Proofs (ZKPs) to enhance security and privacy. In this system, Binius ZKPs ensure that model updates are cryptographically verified without exposing sensitive information, guaranteeing data confidentiality and integrity during the learning process. The lightweight blockchain framework secures the network by creating an immutable, decentralized record of all model updates, thus preventing tampering, fraud, or unauthorized modifications. This decentralized approach eliminates the need for a central aggregator, significantly enhancing system resilience to attacks and making it suitable for dynamic environments like AV networks. Additionally, the system's design includes Byzantine resilience, providing protection against adversarial nodes and ensuring that the global model aggregation process remains robust even in the presence of malicious actors. Extensive performance evaluations demonstrate that the system achieves low-latency, scalability, and efficient resource usage while maintaining strong security and privacy guarantees, making it an ideal solution for real-time federated learning in autonomous vehicle networks. The proposed framework not only ensures privacy but also fosters trust among participants in a fully decentralized environment.
With the demand of cryptocurrencies, threshold ECDSA recently regained popularity. So far, several methods have been proposed to construct threshold ECDSA, including the usage of OT and homomorphic encryptions (HE). Due to the mismatch between the plaintext space and the signature space, HE-based threshold ECDSA always requires zero-knowledge range proofs, such as Paillier and Joye-Libert (JL) encryptions. However, the overhead of range proofs constitutes a major portion of the total cost.
Ron Steinfeld, Amin Sakzad, Muhammed F. Esgin, Veronika Kuchta · 6 authors
We introduce the first candidate Lattice-based designated verifier (DV) zero knowledge sUccinct Non-interactive Argument (ZK-SNARG) protocol, named LUNA, with quasi-optimal proof length (quasi-linear in the security/privacy parameter). By simply relying on mildly stronger security assumptions, LUNA is also a candidate ZK-SNARK (i.e. argument of knowledge). LUNA achieves significant improvements in concrete proof sizes, reaching below 6 KB (compared to >32 KB in prior work) for 128-bit security/privacy level. To achieve our quasi-optimal succinct LUNA, we give a new regularity result for 'private' re-randomization of Module LWE (MLWE) samples using discrete Gaussian randomization vectors, also known as a lattice-based leftover hash lemma with leakage, which applies with a discrete Gaussian re-randomization parameter that is polynomial in the statistical privacy parameter (avoiding exponential smudging), and hides the coset of the re-randomization vector support set. Along the way, we derive bounds on the smoothing parameter of the intersection of short integer solution (SIS), gadget, and Gaussian perp module lattices over the power of 2 cyclotomic rings. We then introduce a new candidate linear-only homomorphic encryption scheme called Module Half-GSW (HGSW), and apply our regularity theorem to provide smudging-free circuit-private homomorphic linear operations for Module HGSW. Our implementation and experimental performance evaluation show that, for typical instance sizes, Module HGSW provides favourable performance for ZK-SNARG applications involving lightweight verifiers. It enables significantly (around 5x) shorter proof lengths while speeding up CRS generation and encryption time by 4-16x and speeding up decryption time by 4.3x, while incurring just 1.2-2x time overhead in linear homomorphic proof generation operations, compared to a Regev encryption used in prior work in the ZK-SNARG context. We believe our techniques are of independent interest and will find application in other privacy-preserving lattice-based protocols.
The hardness of lattice problems offers one of the most promising security foundations for quantum-safe cryptography. Basic schemes for public key encryption and digital signatures are already close to standardization at NIST and several other standardization bodies, and the research frontier has moved on to building primitives with more advanced privacy features. At the core of many such primitives are zero-knowledge proofs. In recent years, zero-knowledge proofs for (and using) lattice relations have seen a dramatic jump in efficiency and they currently provide arguably the shortest, and most computationally efficient, quantum-safe proofs for many scenarios. The main difficulty in using these proofs by non-experts (and experts!) is that they have a lot of moving parts and a lot of internal parameters depend on the particular instance that one is trying to prove.
Daniel Escudero, Antigoni Polychroniadou, Yifan Song, Chenkai Weng
In this work we study the efficiency of Zero-Knowledge (ZK) arguments of knowledge, particularly exploring Multi-Verifier ZK (MVZK) protocols as a midway point between Non-Interactive ZK and Designated-Verifier ZK, offering versatile applications across various domains. We introduce a new MVZK protocol designed for the preprocessing model, allowing any constant fraction of verifiers to be corrupted, potentially colluding with the prover. Our contributions include the first MVZK over rings. Unlike recent prior works on fields in the dishonest majority case, our protocol demonstrates communication complexity independent of the number of verifiers, contrasting the linear complexity of previous approaches. This key advancement ensures improved scalability and efficiency. We provide an end-to-end implementation of our protocol. The benchmark shows that it achieves a throughput of 1.47 million gates per second for 64 verifiers with 50% corruption, and 0.88 million gates per second with 75% corruption.
In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem.
Burhan Ul Islam Khan, Khang Wen Goh, Megat F. Zuhairi, Rusnardi Rahmat Putra · 6 authors
Amidst the rising demands for data security across expansive networks, blockchain technology is witnessing an upsurge in its adoption, particularly within Internet of Things (IoT) applications, services, and smart cities. Blockchains offer an immutable property that bolsters security and aids in the structured management of distributed ledgers. Nevertheless, ensuring scalability remains a formidable challenge, especially within decentralized Ethereum systems. Current methods often fall short of offering tangible solutions, and the scrutiny of Ethereum-based cases reveals persistent deficiencies in addressing scalability issues due to inherent system complexities, dependency on resource-intensive consensus algorithms, lack of optimized storage solutions, and challenges in ensuring synchronous transaction validation across a decentralized network. This paper proposes a foundational scheme underpinned by a unique graph-based topology and hash bindings for nodes that join the system. The proposed scheme establishes an innovative indexing mechanism for all transactions and blocks within the IoT framework, ensuring optimal node accessibility. Transaction and block replications occur over the joining nodes' graphical structure, ensuring efficient subsequent retrieval. A standout feature of the proposed scheme is its ability to enable participating nodes to forgo retaining a complete ledger, making it non-reliant on individual node capabilities. Consequently, this facilitates a broader spectrum of nodes to participate in the consensus system, irrespective of their operational prowess. This study also offers a novel empirical model for Proof-of-Validation (PoV), which reduces computational intricacy and expedites the validation process in stark contrast to prevailing blockchain systems.
Blockchain technology has emerged as a transformative solution in the realm of cybersecurity, addressing critical challenges of data integrity and transparency.The ever-increasing sophistication of cyber threats necessitates robust mechanisms to secure sensitive data and ensure accountability in digital systems.Traditional methods, while effective to some extent, often fail to prevent data tampering and lack comprehensive traceability, leaving organizations vulnerable to breaches.Blockchain's decentralized, immutable ledger offers an innovative approach to overcoming these limitations by ensuring secure data integrity and creating transparent audit trails.This paper explores the application of blockchain technology in enhancing cybersecurity frameworks, emphasizing its role in preventing unauthorized data modification and enabling traceability.By employing cryptographic hashing and consensus mechanisms, blockchain ensures data authenticity while eliminating single points of failure.Its capabilities are particularly relevant for industries with stringent regulatory requirements, such as finance, healthcare, and supply chain management, where data accuracy and accountability are paramount.Moreover, we investigate advanced blockchain models, including private and consortium blockchains, to balance scalability, efficiency, and confidentiality.Integration with complementary technologies like smart contracts and artificial intelligence further extends its utility, enabling automated security protocols and anomaly detection.Despite its promise, blockchain adoption faces challenges, including high energy consumption, scalability issues, and the need for standardization.This study provides a comprehensive analysis of blockchain's potential and limitations in cybersecurity, proposing future directions to optimize its effectiveness.By bridging gaps in technology and implementation, blockchain holds the potential to redefine secure digital interactions, ensuring trust and resilience in increasingly interconnected systems.
This article provides an extensive review of the challenges and opportunities at the intersection of federated learning (FL) and data privacy.Federated learning is a distributed machine learning paradigm enabling collaborative model training across decentralized devices without transferring raw data to a central repository.This method reduces privacy risks and aligns with regulatory compliance while unlocking potential in sensitive domains such as healthcare, finance, and IoT.Despite these advantages, FL faces critical challenges, including susceptibility to adversarial attacks, communication bottlenecks, heterogeneity in devices and data distributions, and limited privacy guarantees.Promising research directions include the integration of differential privacy, secure multi-party computation, and blockchain for enhanced security.This paper underscores the importance of interdisciplinary efforts to overcome these challenges and explores potential applications across domains like personalized medicine, smart grid optimization, and decentralized AI in edge computing environments.It concludes by outlining pathways for future research, emphasizing the need for scalable, efficient, and privacy-preserving FL architectures.
The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seriously threaten its integrity and security. In this paper, we propose a novel double graph-based Ethereum account de-anonymization inference method, dubbed DBG4ETH, which aims to capture the behavioral patterns of accounts comprehensively and has more robust analytical and judgment capabilities for current complex and continuously generated transaction behaviors. Specifically, we first construct a global static graph to build complex interactions between the various account nodes for all transaction data. Then, we also construct a local dynamic graph to learn about the gradual evolution of transactions over different periods. Different graphs focus on information from different perspectives, and features of global and local, static and dynamic transaction graphs are available through DBG4ETH. In addition, we propose an adaptive confidence calibration method to predict the results by feeding the calibrated weighted prediction values into the classifier. Experimental results show that DBG4ETH achieves state-of-the-art results in the account identification task, improving the F1-score by at least 3.75% and up to 40.52% compared to processing each graph type individually and outperforming similar account identity inference methods by 5.23 % to 12.91 %.
Janak Dhokrat, Namita Pulgam, Tabassum Maktum, Vanita Mane
In digital landscape of today’s ongoing world, the imperative for enhanced security in cloud-based data processing is paramount. This paper introduces an innovative framework that seamlessly integrates Homomorphic Encryption and Zero-Knowledge Proofs (ZKPs) to bolster data privacy and confidentiality. This paper explores the technical intricacies, real-world applications, and potential implications of this fusion framework. Homomorphic Encryption empowers computations on encrypted data without compromising privacy, while Zero-Knowledge Proofs offer a mechanism to verify computations without exposing sensitive details. The effectiveness and adaptability of the proposed framework is demonstrated through meticulous analysis and practical deployment in safeguarding cloud-based data processing. The proposed framework marks a significant stride towards creating an environment where data security is unequivocally prioritized.
Ensuring secure and reliable urban metaverse cyberspaces requires addressing two critical challenges, namely, cybersecurity and privacy protection. In the upcoming years, it is anticipated that cybercrime activities will be widespread in this ecosystem, which has trillions of dollars in economic value. This report explores a Blockchain-Facilitated Federated Security-Preserving Deep Learning (BF-FSPDL) authentication and verification method using immersive metaverse devices. Blockchain technology and Federated Learning (FL) are merged not only to eliminate the requirement of a trusted third party for the verification of the authenticity of transactions and immersive actions, but also, to avoid Single Point of Failure (SPoF) and Generative Adversarial Networks (GAN) attacks by detecting the malicious nodes using the majority voting mechanism. The developed approaches in this research have been tested using Motion Capture Suits (MoCaps) in a co-simulation environment with the Proof of Work (PoW) consensus mechanism. The preliminary results prove the viability of employing the proposed approaches in realising the objectives presented in this report. The results suggest that the approaches can prevent impersonation, identity theft, and theft of credentials or avatars promptly before any transactions have been executed. The proposed system will be tested with a larger number of nodes involving the Proof of Stake (PoS) consensus mechanism using several other metaverse immersive devices as future work.
Sujit Biswas, Kashif Sharif, Zohaib Latif, Mohammed J. F. Alenazi · 6 authors
Abstract Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchain's scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain‐based Privacy‐preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs' services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.
In crowd-sourced data aggregation over the Internet, participants share their data points with curators. However, a lack of strong privacy guarantees may discourage participation, which motivates the need for privacy-preserving aggregation protocols. Moreover, existing solutions remain limited with respect to public auditing without revealing the participants’ data. In realistic applications, however, there is an increasing need for public verifiability (i.e., verifying the protocol correctness) while preserving the privacy of the participants’ inputs, since the participants do not always trust the data curators. At the same time, while publicly distributed ledgers may provide public auditing, these schemes are not designed to protect sensitive information. In this work, we introduce two protocols, dubbed Masquerade and zk-Masquerade, for computing private statistics, such as sum, average, and histograms, without revealing anything about participants’ data. We propose a tailored multiplicative commitment scheme to ensure the integrity of data aggregations and publish all the participants’ commitments on a ledger to provide public verifiability. zk-Masquerade detects malicious participants who attempt to poison the aggregation results by adopting two zero-knowledge proof protocols that ensure the validity of shared data points before being aggregated and enable a broad range of numerical and categorical studies. In our experiments, we use homomorphic ciphertexts and commitments for a variable number of participants and evaluate the runtime and the communication cost of our protocols.