Modern cryptographic primitives have evolved from supporting basic to more advanced functionalities, and such schemes are now getting more practical. In this thesis, we identify and rectify some limitations of such cryptographic constructions and their proofs of security. Specifically, we work with functional encryption, secure aggregation, and threshold signature schemes, and observe key functional or security limitations in prior work. Our first focus is functional encryption (FE), which enables function evaluation on encrypted messages using a functional secret key. A different primitive named function-revealing encryption (FRE) allows one to compute a fixed function of the underlying messages using their ciphertexts only. We give formal definitions and construct an inner-product FRE scheme. We also analyze the relationship between FE and FRE. Our second contribution considers secure aggregation, a classic problem that has numerous applications in privacy preserving machine learning. Secure aggregation lets many clients contribute data for aggregation without revealing their individual data. Existing practical protocols either have multiple rounds of interaction between clients and the server or rely on heavyweight cryptographic primitives. We build a non-interactive secure aggregation protocol using a novel combination of inner-product FE and a fully-linear probabilistically checkable proof (FLPCP) system. For this protocol, we use an existing FLPCP system [BBCGIâ19] that we prove satisfies soundness and zero-knowledge properties even when reused for multiple proof instances. Finally, we address a pressing open question: achieving fully adaptive security for the Sparkle+ [CKMâ23] threshold signature scheme. Threshold schemes require t signers to provide partial signatures to form a valid one. Fully adaptive security prevents adversaries from forging signatures even when corrupting up to t-1 signers. While Sparkle+ is secure against static corruption and a limited number of adaptive corruptions, a previous proof of fully adaptive security was shown to be incorrect. We propose a novel hardness assumption under which Sparkle+ satisfies this notion with a tight reduction. We establish hardness of this assumption in the elliptic-curve generic-group model. Our contributions close important gaps in prior work and push advanced cryptographic primitives closer to practice.
This study presents HoloCyberChain, an entropy-driven blockchain framework for decentralized cyber-threat intelligence with formal verification and privacy preservation. Each cyber event is encoded as a four-dimensional entropy fingerprint capturing structural, temporal, behavioral, and propagation uncertainty. A novel Shannonâβ hybrid distance integrates residual-entropy geometry with β-divergence-based distributional separation, yielding a unified statisticalâtopological measure of threat dissimilarity. Residuals are transformed into calibrated novelty probabilities through a logistic uniqueness gate, while a proof-of-detection consensus protocol enables publicly verifiable and Byzantine-resilient acceptance of novel intelligence. Privacy is maintained using zero-knowledge entropy proofs, and accepted threats are organized into a spectral threat-intelligence graph that preserves family-level separability. Simulation experiments demonstrate reliable discrimination (ROC-AUC â0.81, PR-AUC â0.77) and stable calibration under noise and concept drift. Real-world validation using the CICIDS-2017 dataset (225 745 flows, 79 features; 97 718 benign and 128 027 DDoS flows) confirms that DDoS traffic exhibits higher Shannonâβ entropy, with right-shifted density profiles, higher medians, and tighter interquartile ranges relative to benign traffic, indicating that the proposed entropy formulation preserves separability under realistic traffic imbalance. These empirical results align with theoretical guarantees and simulation findings, establishing HoloCyberChain as a reproducible, entropy-verified foundation for scalable and privacy-preserving cyber-threat intelligence sharing.
Syed Raza Abbas, Zeeshan Abbas, Mobeen Ur Rehman, Seung Won Lee
Background Blockchain is increasingly explored as an infrastructure to mitigate data fragmentation, security incidents, and limited patient control in digital health ecosystems. This systematic review analyzed applications of blockchain in smart health systems, with a focus on security models, interoperability approaches, and integration with Internet of Things (IoT) and artificial intelligence (AI). Methods Following PRISMA 2020, PubMed, IEEE Xplore, ScienceDirect, Springer, and Google Scholar were searched for studies published between January 2019 and August 2025 using a predefined strategy combining the terms (âblockchainâ OR âdistributed ledgerâ) AND (âhealthcareâ OR âmedicalâ OR âhealth recordsâ) AND (âsecurityâ OR âprivacyâ OR âinteroperabilityâ); of the 1847 records screened, 26 studies met the eligibility criteria. Results Across these studies, blockchain most consistently strengthened electronic health record management by providing cryptographic access control, tamper-evident and immutable audit trails, and support for cross-institutional data exchange. In four multi-institutional settings, coupling blockchain with AI enabled privacy-preserving federated learning for collaborative diagnostics without centralized data pooling. However, several technical and regulatory constraints were reported, including limited scalability (median throughput <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mo>â</mml:mo> </mml:math> 850 transactions/second vs. >10,000/seconds typically required for national infrastructures), high energy consumption in proof-of-work based schemes, and unresolved tension between immutable ledger storage and data protection rules such as the General Data Protection Regulation âright to be forgotten.â Conclusion Overall, the evidence indicates that blockchain is a credible enabler of secure, interoperable, and patient-governed health data sharing, provided that future deployments incorporate Layer-2 or comparable scalability mechanisms, adopt energy-efficient consensus protocols, and operate within clearer regulatory guidance on the permanence of clinical data.
In digital security, anonymous credential systems are essential to ensure secure and private interactions. These systems have practical applications in various fields, such as online voting, healthcare, and financial services. However, due to high computational overhead and complex architecture, traditional anonymous credential systems often suffer from efficiency and scalability issues. To address these challenges, we propose an innovative approach that combines advanced cryptographic techniques such as randomized BLS aggregate signatures and optimized zero-knowledge proof usage mechanisms to achieve secure and private identity authentication with minimal overhead. We introduce HPPCS (High-Performance Privacy-Preserving Credential System), an anonymous credential framework that leverages randomizable aggregate signature technology to achieve efficiency and strong security. We conducted a security and experimental analysis of the HPPCS framework, and the results showed that HPPCS improves the efficiency of credential generation and verification while ensuring original security. This work establishes a powerful and practical framework for privacy-centric identity authentication systems.
The rapid proliferation of smart home IoT devices has introduced unprecedented cybersecurity vulnerabilities, necessitating scalable and privacy-preserving intrusion detection systems (IDS). Federated Learning (FL) offers a promising decentralized approach by training models locally without sharing raw data, but it remains susceptible to poisoning attacks and relies on a vulnerable central aggregator. This paper presents a novel blockchain-enhanced FL framework tailored for smart home IDS, integrating multiple consensus mechanismsâProof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof-of-Authority (PoA)âfor the first time in this context. Our approach uniquely combines differential privacy (DP) and secure aggregation (SA) within a blockchain-managed workflow to mitigate gradient inversion and membership inference attacks while ensuring tamper-resistant, decentralized trust. Experimental evaluation using the N-BaIoT dataset demonstrates that the proposed system achieves up to 88.3% detection accuracy with manageable latency (~200 ms/round) and formal privacy guarantees ($\varepsilon$=1.0 DP). The framework introduces 52.8% system overhead compared to vanilla FLâa reasonable trade-off for enhanced security and privacy. This work establishes a robust, transparent, and scalable security infrastructure for smart homes, effectively addressing the limitations of both centralized and conventional FL-based IDS.
Abstract With the growing implementation of multi sensor Internet of Things (IoT) and edge AI systems, the concerns over data reliability,privacy, and verifiability have been intensified. Conventional fusion architectures rely on deep learning models that deliverhigh accuracy. However, they fail to ensure that inferences are provably correct or tamper resistant under missing, noisy, oradversarial data conditions. To address these challenges, this paper introduces the Zero-Knowledge Privacy Assured SensorFusion (ZK-PAS Fusion) framework. ZK-PAS Fusion integrates convex bounded imputation, attention driven multi sensorfusion, BiLSTM based temporal modeling, and recursive zero-knowledge proof aggregation within a unified architecture. Theframework assures correctness, privacy, and robustness through cryptographic commitments and circuit level verifiability.Experimental evaluation is performed on two large scale clinical datasets, namely, MIMIC-IV and eICU-CDR. The modeldemonstrates a superior performance and achieves 99.45 % accuracy, 99.57 % F1-score, and an AUROC of 0.989, surpassingstate of the art transformer and diffusion based baselines by up to 5.4 % in accuracy and 6.2 % in F1-score. The proof moduleattains a 40 ms average proving time, 0.4 KB proof size, and â 46 % lower energy consumption compared to state of the art(SOTA) models. These results establish ZK-PAS Fusion as a verifiable, memory efficient, and privacy preserving AI frameworkfor real time, safety critical edge IoT deployments.
This deliverable (D4.4 â Federated AI/ML) defines the architecture, requirements, and enabling technologies for secure and privacy-preserving federated learning within the CONFIDENTIAL6G project. The document specifies how federated AI/ML can be safely deployed across heterogeneous 6G cloudâedge environments, allowing collaborative model training while ensuring that sensitive data remains local and protected throughout the learning lifecycle. The deliverable consolidates background and state-of-the-art insights on federated learning in 6G, identifies key security, privacy, and trust challenges, and derives a set of functional, security, governance, and operational requirements that guide system design. It then presents the overall federated AI/ML architecture developed under this task, which brings together confidential orchestration, federated learning coordination, cryptographic trust mechanisms, and secure execution across cloud-edge environments. The architecture builds on the confidential orchestration foundations established in Deliverable 4.3 and integrates key enablers from WP2âsuch as Decentralized Identifiers, Verifiable Credentials, and Zero-Knowledge Proofsâto support verifiable, policy-driven, and privacy-preserving participation throughout the federated learning lifecycle. Within this architecture, blockchain-enabled aggregation is introduced as a complementary mechanism to strengthen integrity, auditability, and decentralized trust in model management and aggregation workflows by removing single points of failure and providing tamper-evident provenance for AI/ML models. In parallel, the deliverable reports algorithmic contributions that enhance robustness and fairness under non-IID data distributions and device heterogeneity, ensuring that the proposed architecture remains effective under realistic deployment conditions. Finally, the document outlines how the Federated AI/ML integrates with WP5 use cases, demonstrating its relevance for real-world validation scenarios. Overall, this deliverable establishes a coherent and secure federated learning foundation that supports CONFIDENTIAL6Gâs objectives for trustworthy, privacy-preserving AI in next-generation 6G environments.
We present Y.I.N.-MEMORIA, a comprehensive privacy-preserving architecture addressing fundamental vulnerabilities in AI conversation systems across all platforms, including large language model interfaces, enterprise AI assistants, domain-specific chatbots, and agentic AI systems. The system implements mandatory cryptographic ordering enforcement (DP â ZK â BLINDING â HE, or functional equivalents), mathematically proven unique among 24 permutations, achieving 99.37% accuracy for valid authorizations versus 50.7% for invalid attempts (t = 147.3, p < 10âťâľâ°). KEY CONTRIBUTIONS:⢠Hybrid local-cloud storage with zero-knowledge properties ensuring cloud providers mathematically cannot decrypt conversations⢠Enterprise Shadow AI governance achieving 99.7% detection across 50+ services via network pattern analysis without plaintext access⢠Y.A.N.G. constant-time retrieval providing 340Ă timing attack resistance (reduced leakage from 2.72 to 0.008 bits per 1,000 queries)⢠Complete defense taxonomy across 8 attack categories with quantified metrics (92-99% detection rates)⢠Advanced cryptographic primitives including post-quantum aggregate signatures (90% size reduction), threshold token generation, VRFs, adaptive differential privacy (4-tier Îľ system), federated unlearning (SISA), and incremental Merkle tree encryption⢠Four complete deployment architectures (cloud-only, local-only, mobile-only, enterprise gateway) validated across 4 hardware platforms and 5 operating systems⢠Y.I.N. CERTIFY compliance verification layer enabling machine-readable regulatory certificates for GDPR, DORA, EU AI Act, HIPAA, and Singapore's Model AI Governance Framework for Agentic AI⢠Synergistic combination claims and negative exclusion claims establishing comprehensive defensive prior art ENHANCED VERSION 10.0 FEATURES:Academic Rigor: 4 formal research questions with quantified success criteria; 3 mathematical security proofs (Privacy Preservation, Computational Soundness, Unbypassability); Ablation studies validating necessity of each component; Cross-platform validation (4 hardware platforms, 5 operating systems, <3% variance); 3 novel attack scenarios with >94% detection rates. Comparative Analysis: Table comparing against 8 major systems (Federated Learning, CrypTen, TF Privacy, Opacus, PySyft, Microsoft SEAL, Zcash). Y.I.N.-MEMORIA demonstrated as only system providing mandatory DP enforcement, ZK verification for AI governance, 340Ă timing resistance, 99.7% Shadow AI detection, and complete lifecycle coverage. Legal Protection: Doctrine of equivalents coverage (Warner-Jenkinson precedent); Willful infringement notice (Halo Electronics, 3Ă damages); Comprehensive functional equivalents (12 categories); Minimum performance thresholds excluding weak implementations. Reproducibility Commitment: Complete reference implementation under open-source license; Experimental datasets via Zenodo; Cryptographic test vectors for independent verification; Performance benchmarks across all platforms. Scholarly Depth: 38 peer-reviewed citations (65% increase); Comprehensive related work analysis; Explicit limitations and future research directions; Historical non-obviousness evidence. THREE-PHASE AI LIFECYCLE COVERAGE:Y.I.N.-MEMORIA completes the Y.I.N. Architecture's three-phase AI lifecycle: Training (Y.I.N.-LLM, USPTO 63/941,283), Generation (Article 50 Compliance Engine, USPTO 63/957,571), and Usage (Y.I.N.-MEMORIA, USPTO 63/967,805). The Y.I.N. CERTIFY verification layer spans all three phases. Together, these components provide 643 total claims covering every stage where privacy vulnerabilities can emerge in AI systems. EXPERIMENTAL VALIDATION:85-95% bandwidth reduction, 97% conflict resolution, and compliance scores of 94.7-97.3% for GDPR, HIPAA, DORA, EU AI Act, Singapore MGF for Agentic AI, ISO/IEC 42001, CCPA, and NIS2 Directive. IMPACT METRICS:This architecture prevents Shadow AI breaches costing $4.63M average (20% of all data breaches according to IBM's 2025 Cost of a Data Breach Report), addresses the 20M ChatGPT conversation log discovery precedent (NYT v. OpenAI, January 2026), and satisfies Singapore's Model AI Governance Framework for Agentic AIâthe world's first comprehensive government framework for autonomous agents published January 22, 2026 (4 days prior to this work). DEFENSIVE PRIOR ART:This work establishes comprehensive prior art corresponding to USPTO Provisional Application 63/967,805 (438 claims filed January 25, 2026), part of the Y.I.N. Architecture Portfolio (22 applications, 1,360+ total claims). Includes explicit functional equivalents coverage, doctrine of equivalents, and willful infringement notice enabling enhanced damages up to 3Ă under Halo Electronics precedent. Patent Reference: USPTO Application 63/967,805 (Y.I.N.-MEMORIA) License: CC BY-NC-ND 4.0Corresponding Author: ilyesmazari@hotmail.comVersion: 1.0Publication Date: January 26, 2026
Smart mobility services generate large volumes of sensitive location and identity data, raising critical concerns related to privacy leakage, âsecurity vulnerabilities, and trust in large-scale urban deployments. To address these challenges, this paper proposes a blockchain-based âprivacy-preserving framework for smart mobility services that integrates geo-indistinguishability, pseudonymous authentication, Zero-âKnowledge Proofs (ZKPs), and Proof-of-Authority (PoA) consensus into a unified architecture. The framework ensures end-to-end privacy âby combining calibrated location obfuscation with decentralized transaction validation and immutable auditability, thereby mitigating both âinference-based attacks and reliance on centralized trust.â The proposed framework was evaluated using the TAPAS Cologne mobility dataset, comprising 1,000 simulated vehicles and 20 block-âchain validators. Experimental results demonstrate that adversarial inference accuracy is reduced to below 12%, while approximately 75% ânavigation utility is preserved at balanced privacy budgets. Security analysis confirms robust protection against tracking, replay, Sybil, and âcollusion attacks, with replay attack success rates reduced from 70% to 2% through the enforcement of timestamps and nonces, along with âcryptographic verification.â Performance evaluation demonstrates that the framework achieves high throughput (1,200 transactions per second) with sub-second latency ââ(0.8 seconds) under realistic transaction loads. Storage growth is optimized to 2.1 GB per million transactions, and the PoA consensus âmechanism achieves approximately 30% lower energy consumption compared to Proof-of-Stake-based designs. In addition, resilience ex-âperiments confirm Byzantine fault tolerance under up to 30% malicious validator participation, without service degradation.â Overall, the results demonstrate the practical feasibility of deploying the proposed framework in real-world smart mobility ecosystems that ârequire simultaneous privacy preservation, scalability, and energy efficiency. The framework represents a significant step toward trustwor-âthy, privacy-aware, and sustainable smart-city mobility infrastructure, providing a robust foundation for next-generation decentralized mo-âbility servicesâ.
Abstract Healthcare Internet of Things (HIoT) has revolutionized patient care through continuous monitoring and personalized treatment, but it introduces critical challenges in privacy protection, data security, and resource management across heterogeneous devices. Traditional centralized machine learning (ML) approaches face significant limitations due to privacy regulations and security concerns, leading to the emergence of federated learning (FL) and blockchain (BC) as complementary solutions. While FL enables collaborative model training without sharing raw data, and BC provides immutable verification and secure record management. We present SHIELD-Health, a novel framework that synergistically integrates these technologies to create a comprehensive solution for secure analytics in healthcare environments, featuring four key innovations: (1) resource-aware computation that dynamically adapts to device capabilities (2) a multi-layered privacy architecture designed for differential privacy and secure aggregation (3) Byzantine-robust aggregation ensuring model integrity under adversarial conditions, and (4) healthcare-specific optimizations including temporal attention mechanisms for physiological time-series data. Extensive evaluation demonstrates exceptional performance across multiple dimensions, maintaining high accuracy while achieving substantial communication efficiency and energy savings for resource-constrained devices. The framework also shows remarkable resilience against poisoning attacks, and robust performance under challenging non-independent and identically distributed (IID) data distributions common in healthcare scenarios. It represents a significant advancement in privacy-preserving collaborative analytics for sensitive medical applications where security, privacy, and resource constraints are paramount considerations.
Mohammad Shahid, Paritosh Ramanan, Mohammad Fili, Guiping Hu ¡ 5 authors
Analysis of clinical data is a cornerstone of biomedical research with applications in areas such as genomic testing and response characterization of therapeutic drugs. Maintaining strict privacy controls is essential because such data typically contains personally identifiable health information of patients. At the same time, regulatory compliance often requires study managers to demonstrate the integrity and authenticity of participant data used in analyses. Balancing these competing requirements of privacy preservation and verifiable accountability remains a critical challenge. In this paper, we present CoSMeTIC, a zero-knowledge computational framework that proposes computational Sparse Merkle Trees (SMTs) as a means to generate verifiable inclusion and exclusion proofs for individual participants' data in clinical studies. We formally analyze the zero-knowledge properties of CoSMeTIC and evaluate its computational efficiency through extensive experiments. We demonstrate the framework on Huntington's disease and HIV-1 case studies, using simulated CAG-repeat cohorts derived from published summary statistics and published de-identified clinical lab measurements of virus samples. Using two-sample Kolmogorov-Smirnov and likelihood-ratio hypothesis tests, along with logistic-regression-based genomic analyses on the de-identified datasets, we show that CoSMeTIC achieves strong privacy guarantees while maintaining statistical fidelity. Our results suggest that CoSMeTIC provides a scalable and practical alternative for achieving regulatory compliance with rigorous privacy protection in large-scale clinical research.
Hasina Andriambelo, Hery Zo Andriamanohisoa, Naghmeh Moradpoor
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4â11 clients; 160 bytes per client representing 341Ă and 813Ă reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems.
The Internet of Medical Things (IoMT) enables real-time health monitoring and intelligent clinical decision-making by continuously collecting and processing sensitive physiological data from wearable, implantable, and edge-connected devices. However, this data aggregation paradigm introduces critical privacy and security challenges, including data leakage, aggregator misbehavior, and adversarial attacks, while existing frameworks often fail to simultaneously ensure confidentiality, verifiability, and efficiency. To address these limitations, we propose MedGuard, a novel end-to-end secure data aggregation framework for IoMT that synergistically integrates Fully Homomorphic Encryption (FHE) based on the CKKS scheme and Groth16 zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). MedGuard enables healthcare providers to perform complex analytical queries, such as statistical analysis, anomaly detection, and trend forecasting, directly on encrypted data without decryption, ensuring compliance with privacy regulations. By allowing edge nodes to generate cryptographic proofs of correct computation and enabling cloud-based verification, MedGuard eliminates reliance on trusted intermediaries and mitigates insider threats. Our comprehensive evaluation, conducted in a high-fidelity OMNeT++ 6.0.1 simulation environment with 1,000 IoMT devices, 100 edge nodes, and an Amazon EC2 c5.4xlarge cloud server, uses a hybrid dataset combining real-world and GMM-augmented synthetic data. Results show that MedGuard achieves an end-to-end latency of 64.8 ms, a 13.3% improvement over state-of-the-art baselines, communication efficiency of 1.465 GB/s, per-query energy consumption of 1.489 mJ, and sustained throughputs of 1,200 packets/s, 120 aggregates/s, and 1,200 queries/s. These performance gains, combined with a robust [Formula: see text] security level, demonstrate that MedGuard delivers scalable, verifiable, and privacy-preserving analytics for next-generation smart healthcare systems.
We present Y.I.N.-LLM, a privacy-preserving training architecture for Large Language Models that mathematically guarantees non-memorization of training data. The core innovation is the mandatory DPâZKâHE ordering (Differential Privacy â Zero-Knowledge Proof â Homomorphic Encryption) applied to transformer gradients during training. Key results: (1) 2.3% accuracy loss at Îľ=1.0 privacy versus 15-40% with standard DP-SGD; (2) zero extractable training data across all tested attack vectors; (3) native GDPR Article 17 "right to be forgotten" compliance via cryptographic gradient subtraction; (4) EU AI Act Article 50 transparency compliance through verifiable privacy proofs. The Non-Memorization Theorem establishes that for any model M trained with Y.I.N.-LLM parameters (Îľ, δ), the probability of verbatim reproduction is bounded: P[M outputs y | x â training] ⤠e^Îľ ¡ P[M outputs y | x â training]. This transforms copyright defense from argument to mathematics. Y.I.N.-LLM addresses the $10B+ memorization litigation crisis (NYT v. OpenAI, Getty v. Stability AI, Authors Guild v. OpenAI) by providing the first mathematically verifiable non-memorization guarantee with practical accuracy preservation. Patent Protected: U.S. Provisional Application 63/946,118 (filed December 21, 2025).
Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning, enabling model training across decentralized edge devices while preserving data privacy. This methodology is critical for sectors handling sensitive information, such as finance, healthcare, and the Internet of Things (IoT). Despite its benefits, the coordination and communication overhead between distributed nodes remain significant challenges. This paper evaluates the efficacy of REST and GraphQL API architectures in facilitating FL workflows. While REST APIs are favored for their statelessness and simplicity, GraphQL offers enhanced flexibility and efficiency by enabling precise data fetchingâa vital feature for bandwidth-constrained decentralized systems. We provide a comparative analysis of these paradigms across performance, security, and scalability metrics, specifically regarding data synchronization and model aggregation. Finally, we propose design best practices for developing APIs that support robust, compliant, and efficient federated prediction systems.
The convergence of vehicular technology, artificial intelligence (AI), and distributed computing has catalyzed the emergence of the Internet of Vehicles (IoV) as a cornerstone of next-generation intelligent transportation systems (ITS). By enabling vehicle-to-everything (V2X) communication, IoV supports cooperative perception, real-time decision-making, and autonomous driving. However, the reliance on large-scale, data-driven intelligence in IoV exposes systems to critical challenges, including adversarial poisoning, privacy leakage, identity forgery, and the fragility of centralized learning architectures. Federated Learning (FL) has been proposed as a promising paradigm to alleviate some of these issues by enabling distributed model training without centralizing sensitive vehicular data. Nonetheless, conventional FL remains vulnerable to security and trust limitations, particularly in dynamic vehicular environments. This thesis addresses these challenges by designing secure, privacy-preserving, and scalable FL frameworks that leverage distributed ledger technologies and cutting-edge security mechanisms.The thesis advances knowledge through four interconnected contributions. First, two novel optimization-driven poisoning attack models are introduced: PA-PSOSA and PAPSOGA, which combine particle swarm optimization with simulated annealing and genetic algorithms, respectively. These models demonstrate that even a small poisoning budget can substantially degrade global model utility under black-box and clean-label constraints, highlighting the urgency of robust defenses in vehicular FL. Second, a permissioned blockchain-enabled FL (BCFL) framework is proposed, in which consortium edge nodes running Practical Byzantine Fault Tolerance (PBFT) consensus replace the central aggregator. With blockchain integration and data validation mechanisms, this design ensures identity authentication, verifiable audit trails, and improved resilience against poisoning and Sybil attacks, while maintaining high model accuracy under adversarial conditions. Third, the framework is further enhanced to achieve inference-resistance by integrating secure aggregation (SecAgg) and differential privacy (DP), and lightweight with off-chain commitments. This design significantly reduces ledger storage requirements, increases system throughput, and mitigates inference-based privacy risks. Finally, to overcome the scalability limitations of PBFT-based BCFL, a DAG-enabled FL (DFL) framework is developed. By leveraging parallel validation, utility-score-based tip selection, and reputation-weighted aggregation, this framework significantly improves scalability, reduces communication complexity, and enhances robustness in asynchronous vehicular environments.Together, these contributions articulate a coherent progression from exposing vulnerabilities in vehicular FL to constructing secure, privacy-preserving, and scalable frameworks tailored for IoV ecosystems. The findings demonstrate that interdisciplinary integration of optimization theory, cryptography, differential privacy, and distributed ledger technologies is indispensable for trustworthy vehicular intelligence. Beyond theoretical significance, the proposed frameworks offer practical designs for deployment in safety-critical IoV environments. Future research directions include the integration of zero-knowledge proofs (ZKP) for verifiable privacy, adaptive defenses against evolving adversarial strategies, and experimental validation in real-world vehicular testbeds. Collectively, this thesis establishes a foundation for secure federated intelligence in IoV, contributing to the reliability, efficiency, and trustworthiness of next-generation ITS.
A protocol-level transaction tax faces a structural privacy tradeoff: jurisdictional attribution requires releasing aggregates from which an adversary with auxiliary information may attempt to learn properties of the underlying transactions. This paper formalizes the tradeoff and proves two mechanism-level theorems. Theorem 1 establishes that the composition of Pedersen commitments with zero-knowledge validity proofs preserves individual-transaction hiding under standard cryptographic assumptions, with no distributional requirement on transaction amounts. Theorem 2 separates two cohortaggregation threats that prior analyses have conflated and bounds each. Theorem 2a (event detection): against an adversary attempting to detect that an anomalous transaction of magnitude đ occurred in a cohort of size đ, the optimal likelihood-ratio-test.
Prof. M. A. Sayyad, Veerendra Yadav, Dr. Geetika M. Patel, Dr. Prakash Deep ¡ 8 authors
The problem of data privacy, interoperability, cyberattacks, and unauthorized changes of sensitive medical records are becoming critical issues in healthcare information exchange systems. The conventional centralized healthcare designs have single-point failures, inadequate transparency, sluggish data synchronization, and insufficient trust management among dispersed medical organizations. In order to overcome these shortcomings, this paper suggests a Blockchain-Assisted Distributed Artificial Intelligence Framework to Secure Healthcare Information Exchange and Data Integrity. The suggested architecture combines a distributed AI-based healthcare analytics system with blockchain-based immutable ledger systems to provide secure, open, and alteration-free medical data exchange among various healthcare nodes. The automated access control and the secure management of authorization is applied using smart contracts, and intelligent anomaly detection and integrity verification of healthcare transactions are implemented using distributed AI modules. The framework also includes encrypted communication and decentralized consensus systems to promote security and reliability in the context of multi-institutional healthcare settings. Simulated healthcare data based on experimentation shows that the proposed framework has a data integrity verification accuracy of 96.4, anomaly detection accuracy of 92.7 and offers both efficient and secure transaction validation performance at a ratio of 41.3 lower than traditional centralized healthcare systems. The suggested architecture enhances the security of healthcare data and trust management, scalability and interoperability of the next-generation intelligent healthcare ecosystems significantly.
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.
Federated learning across IoT devices must simultaneously protect each deviceâs 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.