Secure data collaboration among mutually distrustful organizations requires more than encrypted storage: it also needs accountable ownership control, auditable access governance, privacy-preserving transaction execution, and reliable settlement when data are exchanged as digital assets. This paper proposes TrustTrade, a unified multi-party secure data management and transaction framework designed for cross-organization data sharing, trading, and compliance-sensitive analytics. TrustTrade integrates policy-bound data capsules, a tamper-evident provenance ledger, adaptive threshold escrow, verifiable data-payment settlement, and selective audit with revocation rebinding. On four real-dataset-derived workloads, TrustTrade reaches a $90.4$–$94.8\%$ settlement rate, with a $92.5\%$ average that is $6.4$ percentage points higher than the strongest baseline average. Under adversarial request injection, TrustTrade reduces unauthorized release to $0.31\%$ and atomicity violation to $0.38\%$, corresponding to $93.6\%$ and $93.0\%$ reductions compared with Plain-Market, respectively; compared with Fixed-Escrow, unauthorized release is reduced by $77.4\%$. TrustTrade also achieves $96.7\%$ dispute-resolution accuracy while maintaining practical settlement latency. These results indicate that jointly designing secure data management and secure data transaction protocols offers a practical path toward trustworthy multi-party data ecosystems.

This paper presents a novel approach to enhance data security and processing efficiency in smart home systems by integrating blockchain technology with the random forest algorithm. By leveraging the Hyperledger Fabric platform, we implemented smart contracts to securely manage data transmission and storage, while the random forest algorithm was utilized for accurate data classification and prediction. The experimental results demonstrate that this integrated approach significantly improves data integrity and immutability, achieving high classification accuracy and system efficiency. The findings highlight the potential of combining blockchain and machine learning techniques to create a robust and reliable solution for managing and processing data in smart home environments.

Sentiment analysis of user reviews plays a crucial role in e-commerce platforms for understanding customer satisfaction, monitoring product reputation, and improving service quality. Traditional sentiment analysis methods, including lexicon-based approaches and conventional machine learning algorithms, often struggle with capturing contextual semantics, handling domain-specific expressions, and addressing the linguistic complexity inherent in user-generated content such as informal language, sarcasm, and mixed sentiments. This paper proposes a novel sentiment analysis model that integrates BERT (Bidirectional Encoder Representations from Transformers) for contextual text encoding with an enhanced Transformer architecture specifically designed for fine-grained sentiment classification in e-commerce reviews. Our approach incorporates four key innovations: (1) domain-adaptive pre-training on 2 million unlabeled e-commerce reviews to capture domain-specific vocabulary and linguistic patterns, (2) sentiment-aware attention mechanisms with learnable query vectors that explicitly identify and emphasize sentiment-bearing expressions such as opinion words, negations, and intensifiers, (3) multi-level feature aggregation that combines token-level representations from different BERT layers through hierarchical attention to capture both syntactic and semantic information, and (4) focal loss optimization to effectively handle the class imbalance problem commonly observed in real-world review datasets. Our model leverages BERT's pre-trained bidirectional language representations to capture deep semantic features and contextual dependencies from reviews, while the enhanced Transformer layers with sentiment-aware attention enable effective modeling of long-range dependencies and complex sentiment-bearing patterns across different linguistic granularities.

Federated learning (FL) enables collaborative model training without centralizing raw data, yet practical deployments remain vulnerable to security threats such as Byzantine model poisoning, stealthy backdoor implantation, and integrity attacks that exploit the opacity of client updates. This paper presents SecureFedGuard, a security-centric FL framework that introduces a novel combination of (i) dual-view update authentication that binds each client update to a lightweight stochastic gradient fingerprint, enabling server-side integrity screening without accessing client data, and (ii) backdoor-resilient aggregation driven by cross-round spectral forensics and adaptive coordinate-wise trimming guided by an estimated benign subspace. SecureFedGuard is designed to be compatible with secure aggregation and does not require trusted hardware, public datasets for pretraining, or expensive per-client verification. We provide a simple robustness analysis that clarifies when benign updates dominate the estimated subspace under mixed benign/malicious participation. Experiments on real FL benchmarks (vision and language) under diverse threat models show that SecureFedGuard substantially improves clean accuracy and backdoor attack success rate compared with strong baselines, while adding modest communication and computation overhead. These results suggest a practical path toward integrity-preserving and backdoor-resistant FL without weakening the privacy boundary between clients and the server.

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their raw data, making it a promising paradigm for privacy-sensitive generative applications. However, diffusion models trained in federated environments remain vulnerable to backdoor, evasion, and model-poisoning attacks, which can degrade generative quality or induce attacker-specified behaviors. Existing Byzantine-robust aggregation methods primarily mitigate anomalous client updates but do not address coordinate-localized poisoning or adversarial perturbations in the diffusion latent space. Meanwhile, existing defenses for diffusion models are generally designed for centralized settings and therefore cannot directly handle malicious client updates in federated training. To address these limitations, we propose VDFedDiff, a multi-level defense framework for federated diffusion models. VDFedDiff integrates three complementary components: Adaptive Trust Scoring (ATS), which dynamically reduces the influence of anomalous client updates; Adversarial Gradient Filtering (AGF), which suppresses coordinate-wise malicious deviations through median- and median-absolute-deviation-based filtering; and Latent-Space Purification (LSP), which introduces time-aware stochastic regularization to weaken adversarial alignment during local diffusion training. Experiments on CIFAR-10, CelebA, and LSUN-Bedroom under backdoor, evasion, and model-poisoning attacks demonstrate that VDFedDiff consistently improves attack resilience while preserving generative quality. On non-IID CIFAR-10 under combined backdoor and model-poisoning attacks, VDFedDiff reduces the FID from 46.8 to 28.3 and the attack success rate from 72.5% to 23.8% compared with undefended FedAvg. It also introduces only 6.1% additional per-round computation time and no additional communication overhead in the evaluated setting. These results demonstrate the effectiveness of combining client-level, coordinate-level, and latent-level protection for securing federated diffusion training.