Vehicle platoon, an increasingly significant technology in vehicular ad hoc networks (VANETs), effectively reduces energy consumption and environmental pollution, mitigates traffic congestion, and enhances road capacity and traffic safety. Collaborative communication between platoons is available to promote the reliability of data dissemination, ameliorate driving strategies, and further strengthen traffic efficiency. Nevertheless, existing efforts for secure multi-platoon data dissemination still face the following issues: (i) lack of a direct authorization strategy for platoons working on different systems; (ii) no effective mechanism to protect vehicle privacy during platoon communications; (iii) absence of a practical verification approach for cross-system ciphertext transformation. This paper builds a privacy-preserving and verifiable cross-system authorization (PVCA) scheme to mitigate the above issues. Specifically, we first put forth the optimized anonymous identity-based broadcast encryption (IBBE) and policy-hiding attribute-based encryption (ABE) protocols to adapt platoon communications while protecting identity and attribute privacy. After that, the authorized token bridging the proposed protocols is designed to enable ciphertexts of IBBE format to be transformed into new ciphertexts of ABE format, enabling flexible authorization. Furthermore, inspired by the Fujisaki-Okamoto transformation, we devise an efficient zero knowledge proof of knowledge protocol, making our PVCA achieve verifiability and fairness. Rigorous security proof and analysis demonstrate that our PVCA is not only secure against chosen plaintext attacks and collusion attacks, but also satisfies the necessary security requirements. We implement a prototype of PVCA (on a desktop computer and Raspberry Pi) and provide a simulation utilizing NS-2 to validate its feasibility for platoon communications in VANETs. The results indicate that, compared to the state-of-the-art ciphertext transformation solutions, PVCA reduces the running time for original ciphertext generation, transformation, and decryption by at least 91.57%, 49.87%, and 50%, respectively, while compressing the original ciphertext and authorization token sizes by over 78.07% and 15.20%.
Democratic governance depends fundamentally on the integrity of electoral processes, as public trust in democratic institutions is only as strong as the systems that underpin them. Conventional voting mechanisms whether paper-based or early digital systems face growing scrutiny regarding transparency, auditability, and resistance to manipulation, with high-profile electoral controversies in recent years intensifying calls for more robust and verifiable alternatives. This paper proposes and evaluates a blockchain-powered electronic voting (e-voting) system designed to overcome these longstanding limitations by leveraging the core properties of distributed ledger technology: immutability, decentralization, and cryptographic verifiability. The proposed architecture integrates Ethereum-based smart contracts for automated ballot management, zero-knowledge proof (ZKP) protocols for voter privacy, and a permissioned blockchain layer for regulatory compliance. Together, these components form a cohesive framework that seeks to balance openness with accountability. A comparative evaluation against existing solutions demonstrates measurable improvements in security, voter anonymity, and system auditability. Scalability constraints, regulatory considerations, and real-world deployment challenges are also examined with candor, acknowledging that no technological solution is without friction. The analysis concludes that blockchain-based e-voting represents a technically viable and socially consequential advancement in democratic infrastructure one with the potential to restore and reinforce public confidence in electoral outcomes worldwide.
With the rapid increase of terminal devices in the Internet of Things (IoT), it has become a significant challenge to achieve real-time and privacy-preserving data aggregation. To address this challenge, edge computing has emerged as an effective paradigm to reduce latency, where a privacy-preserving data aggregation scheme is exploited to preserve data privacy. However, most existing privacy-preserving data aggregation schemes are limited by fixed data dimensions, low scalability, and high communication or computational overhead. To address these shortcomings, this paper proposes a multidimensional privacy-preserving data aggregation scheme that supports flexible dimension expansion and privacy protection in edge computing systems. The scheme integrates the Chinese Remainder Theorem (CRT) with an elastic modulus set to efficiently pack multidimensional data. This design enables terminal devices to add new data dimensions without interrupting current operations or modifying historical data. Furthermore, by exploiting Bulletproofs-based zero-knowledge proofs and Bellare-Neven (BN) signatures with half-aggregation, the proposed scheme enables lightweight and scalable batch verification of data integrity and authenticity. These mechanisms effectively reduce the verification workload and communication bandwidth in large-scale deployments. In addition, an optimized Paillier homomorphic encryption algorithm is used to enable efficient aggregation of encrypted multidimensional data. Experimental results and theoretical analysis show that the proposed scheme significantly reduces computational and communication costs compared with existing methods.
Blockchain technology, in simple words, is an innovative force that democratizes the methodologies of financial transactions by creating safe, traceable, and unalterable digital data. This research investigates how blockchain increases financial transparency in banking, government, and supply chain management for different sectors. It identifies block chain’s core features: decentralized ledgers, real-time auditing, and transparent data sharing, in total reducing information asymmetry and thus fraud, increasing public trust. This study will focus on the role of block chain in financial reporting, as immutable transaction records ensure audit-free error-free error checks and compliance with regulatory standards. The primary use cases for this are anticorruption government procurement systems, banking networks improving fraud detection, and supply chain platforms ensuring product traceability. Smart contracts integrated into financial processes help reduce intermediaries and promote accountability. The paper concludes with an overview of emerging trends in zero-knowledge proofs, decentralized finance, and blockchain-based governance systems that may transform the standards of transparency. Some policy recommendations for leaders are investment in blockchain research, the development of regulatory frameworks, and fostering cross-industry collaboration. Blockchain technology is expected to redefine financial transparency through accountability, fraud reduction, and increased public trust in digital economies.
This study focuses on the core needs of consumer data privacy protection in the context of the digital economy and creates a blockchain-based privacy and security architecture. Through a layered design, this architecture effectively combines data collection, blockchain core, privacy computing, smart contracts, and application integration modules. It integrates key techniques such as zero-knowledge proofs, homomorphic encryption, and decentralized identity to ensure that data is encrypted and stored throughout its creation and destruction, implements meticulous access rights management, and implements a verifiable audit process. The dataset used in this experiment is the 2024 CMS market county-level administrative district public dataset in the United States. In an environment simulating actual business pressures, the privacy protection effectiveness, system scalability, and computational and storage costs of this proposed system are tested. Comparisons are made with two typical implementations. While ensuring differential privacy and k-anonymity, the proposed system improves data transmission speed, reduces processing latency, and reduces storage consumption. This demonstrates the potential and superior performance of this system across multiple entities and industries. This study provides a practical and feasible technical implementation for blockchain-driven consumer data privacy protection and offers a verifiable engineering reference for data governance and cross-industry data sharing in the United States.
Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi · 8 authors
The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability.While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality-either exposing sensitive data or obscuring it beyond regulatory scrutiny.To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions.A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification.A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure.Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance.End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims.The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.
To harness the commercial potential of medical data, various blockchain-based data-sharing and exchange platforms have been proposed. A key challenge is accurately tracing the provenance and transformations of medical data assets throughout the exchange process. Existing methods cannot facilitate exchanges of publicly hosted datasets while maintaining fairness, as they require revealing keys to the blockchain during interactions. This paper presents MEDET, a novel data exchange scheme that ensures traceability of medical data assets while protecting data privacy and guaranteeing exchange fairness. MEDET leverages zero-knowledge proofs to securely verify transformations within medical datasets and confirm data authenticity. Unlike previous schemes, MEDET supports both simple data exchanges and detailed tracking of data transformations and transaction histories, aiding in the provenance and value assessment of medical records. Additionally, MEDET features a key-secure protocol for fair exchange without disclosing symmetric keys. Compared to existing fair exchange schemes, MEDET uniquely ensures the privacy of publicly hosted data while simultaneously upholding the exchange fairness. The security analysis of MEDET demonstrates its security and privacy properties. The evaluation of MEDET indicates that it outperforms existing schemes in tracking data transformations and facilitating exchanges.
As privacy concerns intensify in data-driven systems, this article presentsPriMa$\chi $, a hybrid framework that combines a genetic algorithm (GA) and reinforcement learning (RL) to optimize the privacy–utility tradeoff in differential privacy (DP) through explicit adaptive privacy–utility control. PriMa$\chi $adaptively selects perturbation configurations to minimize the privacy budget$(\varepsilon)$while preserving data utility$({\mathcal {U}})$. To support verifiable privacy-preserving analytics in decentralized environments, we further integrate PriMa$\chi $with a privacy-aware smart-contract framework that enables on-chain DP enforcement and zero-knowledge proof (ZKP) verification. The framework supports structured, transactional, and spatiotemporal workloads, including decentralized finance, electronic health records, census analytics, and location services. An interleaved Petri net model is used to formally verify privacy-aware state transitions in the smart-contract workflow. Experimental results show that PriMa$\chi $achieves utility of at least 80% under dataset-dependent privacy budgets in the range$0.003 \leq \varepsilon~\leq\unicode{0x0142}.43$, while also effectively mitigating model-extraction, membership-inference, and privacy-budget-exhaustion attacks. These results demonstrate that PriMa$\chi $provides adaptive, auditable, and practically deployable privacy protection for decentralized analytics.
Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, practical deployments are often limited by significant communication overhead and vulnerability to Byzantine poisoning attacks. Existing defenses typically rely on post-hoc anomaly detection, but executing complex distance metrics or clustering on encrypted, sparsified updates creates a substantial computational burden for the aggregation server. We present a privacy-preserving FL framework that addresses these challenges by integrating Top-ksparsification, non-interactive zero-knowledge proofs (NIZKPs), and homomorphic encryption. Instead of relying on expensive ciphertext distance computations, our architecture uses a pre-aggregation global mask sign vector, generated through majority voting, to filter anomalous updates. This mechanism treats unselected gradient coordinates as explicit zero-votes, which mitigates malicious coalitions attempting to manipulate disjoint parameter subsets. A local error feedback mechanism ensures that heterogeneous client updates align over successive training rounds. Combined with NIZKPs to enforce coordinate-wise magnitude bounds, the framework provides Byzantine resilience without increasing communication costs or compromising privacy. Evaluations on MNIST and CIFAR-10 show that our approach maintains high communication efficiency and robustness. Under a 40% malicious client poisoning attack and a 50% sparsification ratio, the framework achieves final accuracies of 92.14% and 63.20%, respectively, demonstrating its effectiveness in bandwidth-constrained, hostile environments.
Federated learning across IoT devices must simultaneously protect each device’s update from disclosure, prevent malicious participants from biasing the global model, and hide which devices are participating from outside observers. Existing systems typically address only a subset of these goals: secure aggregation hides individual updates but cannot validate them, plaintext-based robust filtering requires the server to see updates, and most cryptographic pipelines ignore timing privacy. This paper presents TriSAFE, a protocol composition for IoT federated learning with a single coordinating server and three threshold helpers. The server holds no decryption key. TriSAFE combines four mechanisms that are usually studied in isolation: (i) encrypted client updates accompanied by zero-knowledge proofs that each coordinate lies within a bounded range; (ii) a new lightweight binding step (the plaintext-equivalence protocol, PEP) that cryptographically ties the values proven in zero knowledge to the exact ciphertext later aggregated by the server, closing a substitution gap left by range proofs alone; (iii) helper-added differential privacy noise applied homomorphically before any decryption, so the server only ever sees a noised aggregate; and (iv) fixed-cadence batching with calibrated cover traffic to hide participation from passive network observers. Across two IoT intrusion-detection benchmarks (Edge-IIoTset and N-BaIoT) and MNIST, TriSAFE keeps accuracy within 0.1-2.1 percentage points of the no-attack baseline under Byzantine, label-flip, FANG, and time-delay attacks, with attack success rate below 1% (<0.1% for FANG). Timing inference by a passive observer drops close to chance, and the end to end overhead is 7-36% relative to a non-defended baseline. On MNIST, TriSAFE achieves 89-91% accuracy, 15-17 points above the MODEL benchmark under the same attack suite. The design is practical for gateway-assisted IoT deployments under the assumption that the coordinator does not collude with two helpers and that at least two helpers contribute honest DP noise.