Adaptive Differential Privacy Cox-MLP Model Based on Federated Learning

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.

Differentially private graph neural networks for graph classification and its adaptive optimization

HierFedPDP:Hierarchical federated learning with personalized differential privacy

The trade-off between privacy and accuracy presents a challenge for current federated learning (FL) frameworks, hindering their progress from theory to application. The main issues with existing FL frameworks stem from a lack of interpretability and targeted privacy protections. To cope with these, we proposed Disentangled Federated Learning for Privacy (DFLP) which employes disentanglement, one of interpretability techniques, in private FL frameworks. Since sensitive properties are client-specific in nature, our main idea is to turn this feature into a tool that strikes the balance between data privacy and FL model performance, enabling the sensitive attributes to be private. DFLP disentangles the client-specific and class-invariant attributes to mask the sensitive attributes precisely. To our knowledge, this is the first work that successfully integrates disentanglement and the nature of sensitive attributes to achieve privacy protection while ensuring high FL model performance. Extensive experiments validate that disentanglement is an effective method for accuracy-aware privacy protection in FL frameworks.

Early diagnosis of thoracic diseases using chest x-ray imaging remains a critical challenge, particularly in resource-constrained healthcare environments where data sharing is restricted due to privacy concerns. Federated learning (FL) offers a decentralized solution by enabling collaborative model training without sharing sensitive patient data. However, integrating privacy-preserving mechanisms such as differential privacy (DP) introduces additional challenges related to performance degradation and computational overhead. In this study, we present a unified FL framework for multi-label thoracic disease classification using multiple convolutional neural network (CNN) architectures, including ResNet50, DenseNet169, EfficientNet variants and MobileNetV3. Unlike prior studies focusing on single-model evaluation, this work provides a controlled comparative analysis under identical FL settings and investigates the impact of client scalability (5-10 clients) on model performance. Furthermore, we conduct a comprehensive empirical analysis of the privacy utility trade-off by integrating DP with varying privacy budgets (ε=1, 15 and 30). Experimental results on the CheXpert and NIH Chest x-ray14 datasets demonstrate that the proposed EfficientNet-B3-based federated model achieves a mean AUC of 0.8027, while maintaining robustness across decentralized settings. The integration of DP leads to a predictable reduction in performance, with mean AUC ranging from 0.60 to 0.64, highlighting the inherent trade-off between privacy and diagnostic accuracy. The findings emphasize the practical viability of FL for privacy-sensitive medical imaging applications and provide insights into model selection, scalability and privacy configuration for real-world deployment. The source code for this study is publicly accessible at https://github.com/Zulqarnain8-8/FEDERATED_LEARNING_FOR_THORACIC_DISEASE_CLASSIFICATION.

Personal data privacy is a great concern by governments across the world as citizens generate huge amount of data continuously and industries using this for betterment of user centric services. There must be a reasonable balance between data privacy and utility of data. Differential privacy is a promise by data collector to the customer’s personal privacy. Centralised Differential Privacy (CDP) is performing output perturbation of user’s data by applying required privacy budget. This promises the inclusion or exclusion of individual’s data in data set not going to create significant change for a statistical query output and it offers -Differential privacy guarantee. CDP is holding a strong belief on trusted data collector and applying global sensitivity of the data. Local Differential Privacy (LDP) helps user to locally perturb his data and there by guaranteeing privacy even with untrusted data collector. Many differential privacy algorithms handles parameters like privacy budget, sensitivity and data utility in different ways and mostly trying to keep trade-off between privacy and utility of data. This paper evaluates differential privacy algorithms in regard to the privacy support it offers according to the sensitivity of the data. Generalized application of privacy budget is found ineffective in comparison to the sensitivity based usage of privacy budget.

In response to the privacy leakage risks inherent in the big data processing of power user personas, propose a collaborative optimization‐based joint privacy protection mechanism for Differential Privacy (DP) and Secret Sharing (SS). This method reveals the inherent correlation between DP noise parameters and SS sharding strategy and designs an adaptive noise sharding mapping mechanism to achieve joint optimization of privacy protection and communication overhead. Based on this mechanism, a specific privacy protection scheme for power user personas is designed. Experimental results demonstrate that the DP + SS privacy protection method designed in this study achieves an accuracy rate of 93.14% on the MNIST dataset and maintains an accuracy rate of 92.06% across multiple datasets, indicating that the scheme effectively quantifies the level of privacy protection. The privacy protection level of the scheme is quantitatively defined through differential privacy budget ε ∈ [0.11,0.15], secret sharing ( k = 3, n = 5) threshold mechanism, and privacy leakage probability δ = 10 −5 . The privacy budget is dynamically allocated through the correlation between differential privacy noise variance and the minimum reconstruction threshold of secret sharing, ensuring that privacy risks are controllable and quantifiable. At the same time, the average training time for servers is about 186.4 s, which refers to the total end‐to‐end training time in a four‐server cluster environment. Compared with a single server encryption scheme, the efficiency is improved by 48.6%. The communication overhead has increased to 10.6GB, which is the total chip transmission overhead per 10,000 users. However, the calculation stability can be maintained through load balancing, indicating that the scheme has certain feasibility. The aforementioned research results not only provide a viable applied cryptographic technology solution for privacy protection in power big data but also offer new insights into balancing privacy protection strength and data availability. More importantly, the joint privacy protection mechanism proposed by the research institute can serve as a fundamental enabling technology for the infrastructure of a secure smart grid, providing quantifiable and trustworthy privacy and security guarantees for advanced data‐driven applications such as precise load prediction, demand‐side response, and collaborative optimization of distributed energy, thereby supporting the reliable and intelligent development of the smart grid.

Differential privacy is often used in location privacy protection because of its strict reasoning and proof privacy guarantee. When users make continuous location query, it will cause noise superposition, which leads to the decline of query accuracy. At present, although differential privacy based on rule tree structure can reduce the query error, it will generate a lot of invalid zero nodes. The data structure is too large, and more improvement in the query accuracy can be further investigated. In this paper, we proposed a differential privacy location privacy protection method based on generative adversary network. Firstly, the definition of location data privacy protection under differential privacy mechanism is given, and then resume density aware network under differential privacy mechanism. Based on density aware network, the privacy protection problem of location data can be transformed into the distribution of fitting trajectory length. Finally, we use Markov chain to generate a new trajectory, and introduce the generative adversarial network to construct a set differential privacy protection method. Compared with other methods to improve the accuracy of differential privacy query, this method can effectively reduce the problem of query accuracy decline caused by noise superposition in continuous query, and can adapt to lbs location query service in different density environments.KeywordsGenerative adversarial networkLocation differencePrivacy protectionMarkov chainDensity aware network

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL.

With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.

In distributed learning, the End-Edge-Cloud architecture is gaining widespread adoption. However, the cross-layer data interaction in EEC systems significantly increases the risk of privacy breaches. To address this challenge, this paper proposes a novel dynamic privacy-protection framework named MetaDP-HE. The framework is designed to enhance privacy protection while maintaining model performance. It integrates meta-model guided differential privacy (MetaDP) with CKKS homomorphic encryption that supports floating-point operations. A dynamic coordination mechanism is introduced to optimize the parameter configurations between MetaDP and HE. Specifically, clients use the meta-model to predict privacy budgets based on data sensitivity and adjust the noise in differential privacy accordingly. Edge servers then dynamically adjust the encryption parameters of homomorphic encryption based on the noise level, achieving adaptive regulation of encryption strength. The combination of layered encryption and dynamic parameter optimization enables the system to ensure privacy protection and efficient operations when handling data at different levels. Experimental results show that MetaDP-HE outperforms traditional single-privacy methods in both privacy protection and model performance, validating its effectiveness and applicability in practical scenarios.

Federated learning is a distributed machine learning technique that allows multiple devices to collaborate on learning a shared model without exchanging data. It can be used to improve model accuracy while protecting user privacy. However, traditional federated learning is vulnerable to attacks from generative adversarial networks (GANs). As a new privacy protection method, differential privacy enhances privacy protection capabilities by sacrificing some data accuracy. To optimize the privacy budget allocation scheme in traditional differential privacy, we propose a differential privacy method called ADP-FL, which dynamically adjusts the privacy budget based on Newton’s Law of Cooling. While maintaining the overall privacy budget, it dynamically tunes adaptive parameters to improve training accuracy. Additionally, we propose an asynchronous federated learning aggregation scheme that combines privacy budget with data freshness, thereby reducing the impact of differential privacy on accuracy. We conducted extensive experiments on differential privacy algorithms based on Gaussian mechanisms and Laplace mechanisms. The experimental results show that, under the same privacy budget, our algorithm achieves higher accuracy and lower communication overhead compared to the baseline algorithm.

Association analysis is an important task in data analysis to find all co-occurrence relationships (i.e., frequent itemsets or confident association rules) from the transactional dataset. An association rule can help people better discover patterns and develop corresponding strategies. The process of data analysis can be highly summarized as a set of queries, where each query is a real-valued function of the dataset. However, without any restriction and protection, accessing the dataset to answer the queries may lead to the disclosure of individual privacy. In this paper, we propose and implement the association rule mining with differential privacy algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items, and uses random truncation and uniform partition to lower the dimensionality of the dataset. We also stabilize the noise scale by adaptively allocating the privacy budgets, and bound the overall privacy loss. In addition, we prove that the association rule mining with differential privacy algorithm satisfies ex post differential privacy, and verify the utility of our association rule mining with differential privacy algorithm through a series of experiments. To the best of our knowledge, our work is the first differentially private association rule mining algorithm under multiple support thresholds.

The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for differential privacy is currently inconclusive. This paper’s main contribution is finding a balance between privacy and accuracy. The training data of deep learning models may contain private or sensitive corporate information. These may be dangerous to attacks, leading to privacy data leakage for data sharing. Many strategies are for privacy protection, and differential privacy is the most widely applied one. Google proposed a federated learning technology to solve the problem of data silos in 2016. The technology can share information without exchanging original data and has made significant progress in the medical field. However, there is still the risk of data leakage in federated learning; thus, many models are now used with differential privacy mechanisms to minimize the risk. The data in the financial field are similar to medical data, which contains a substantial amount of personal data. The leakage may cause uncontrollable consequences, making data exchange and sharing difficult. Let us suppose that differential privacy applies to the financial field. Financial institutions can provide customers with higher value and personalized services and automate credit scoring and risk management. Unfortunately, the economic area rarely applies differential privacy and attains no consensus on parameter settings. This study compares data security with non-private and differential privacy financial visual models. The paper finds a balance between privacy protection with model accuracy. The results show that when the privacy loss parameter ϵ is between 12.62 and 5.41, the privacy models can protect training data, and the accuracy does not decrease too much.

BackgroundHealth physical examinations play a crucial role in early detection of cancer and chronic disease. However, privacy concerns limit the utilization of this kind of data for health interventions and research. Synthetic data methods based on differential privacy are increasingly used to create complete datasets that protect privacy while enabling data analysis and result interpretation. Hence, the use of synthetic algorithms based on differential privacy for privacy protection of physical examination data is a promising research direction.MethodsThree synthetic algorithms, PrivBayes, PeGS, and DP-Gibbs were used to generate complete synthetic datasets that adhere to differential privacy standards using physical examination data composed of categorical data, which compared with the existing algorithm Private-PGM.ResultsCompared with the existing algorithm, DP-Gibbs can provide privacy preserving capacity of 4.686 (ε = 0.5), while the existing algorithm only with 2.012. In addition, DP-Gibbs provides 0.620 of precision, 0.539 of F1-score, 0.342 of Kappa Coefficient, and 0.765 of AUC-score. The corresponding statistical results of existing algorithm are 0.520, 0.321, 0.188 and 0.695.ConclusionsThe main contributions of this study are the exploration of combination models incorporating different noise forms and Bayesian synthetic algorithms, alongside a comparative analysis against existing algorithms. This study explored the balance between privacy protection and data utility under different levels of privacy protection, and DP-Gibbs offers more stable technical support for de-identifying physical examination data prior to sharing and analysis, which realized the mining and application of a wider range of medical data under the requirements of privacy protection. By leveraging this effective privacy protection technique, clinical researchers can extract valuable insights on diseases and population health from the physical examination data without the risk of leaking private information.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12911-025-03109-1.