The integration of vehicular networks with cloud and IoT infrastructures requires secure and lightweight authentication to ensure privacy and reliability in highly dynamic environments. This paper proposes an adaptive multi-factor authentication protocol that combines biometrics, passwords, and elliptic curve cryptography (ECC) to provide strong mutual authentication between vehicles, roadside units (RSUs), and cloud servers. A Reed–Solomon–based fuzzy extractor is incorporated to address biometric variability, ensuring stable key generation without compromising efficiency. The protocol achieves resilience against impersonation, replay, stolen credential, and man-inthe-middle attacks, supported by a formal proof under the Random Oracle Model (ROM). To validate practicality, the scheme is implemented in Python and evaluated in a realistic vehicular simulation environment using OMNeT++, SUMO, and Veins with real-world road network data. Experimental results demonstrate low authentication delay, reduced communication and computational overhead, and high scalability under varying vehicle densities, confirming its suitability for secure and realtime vehicular cloud environments.

Preserving an individual privacy in the Artificial Intelligence(AI)era is a pressing challenge, as traditional anonymization techniques often fall short againstAI-driven threats. WhileAIhas enabled transformative advancements across sectors such as healthcare, finance, aviation, smart cities, and the Internet of Things (IoT), it also introduces new privacy challenges. In particular,AI-driven background knowledge attacks occur when adversaries combine auxiliary information from external sources such as public records, social media, or previously leaked datasets with advancedAItechniques to re-identify individuals or infer sensitive details from anonymized datasets, rendering conventional defenses less effective. In this study, we presentPrivacyGuard, an automated framework that integrates machine learning with traditional anonymization to address these risks.PrivacyGuardemploys K-Nearest Neighbor and Random Forest classifiers to identify and evaluate privacy risk of each data tuple inAI-driven attack scenarios. Following risk assessment, Laplace-based differential privacy is applied to mitigate identified risks, while fuzzy logic forms equivalence groups that reflect varying risk levels. Experimental validation on the 1:M Adult dataset (an individual having multiple records) demonstrates thatPrivacyGuardachieves 96.47% accuracy and mitigates approximately 42.21% of the identified risks, confirming its effectiveness in balancing privacy and data utility against emergingAI-driven threats.

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model$l_{c}, l_{s}$-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack ($V_{c0}$) and a Vulnerable sensitive attribute attack ($V_{sa}$) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.

Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.

Abstract Every individual in our technologically evolved world needs proper data security. The procedure of exchanging medical information is increasingly concerned with data privacy. Many techniques have been offered for preserving data security. These techniques use approaches such as ‐anonymity, ‐diversity, and others. However, such solutions are vulnerable to attribute disclosure, homogeneity, and background knowledge risks due to their syntactic nature. In this work, we describe a safe and secure architecture and semantic approach for data sharing that is based on blockchain, local differential privacy (LDP), and federated learning (FL). The proposed framework generates an atmosphere devoid of trust in which data owners are no longer required to have trust in the controllers. The FL models enable the whole network to decentralize its data‐driven learning. Interplanetary file system (IPFS) is used to provide data security in a distributed environment because each file in IPFS has a digital fingerprint that is computed using a cryptographic hash function on the file's whole contents. Due to the rigorous privacy guarantee, data owners no longer need to be worried about the security of their data. The proposed model's assessment parameters include latency, throughput, privacy, and accuracy. The data privacy of the proposed model is protected via LDP and FL, and its latency and throughput communication transactions on permissioned blockchain are calculated and compared with those of the benchmark model. The findings indicate that the proposed model delivers 85% more accurate privacy than the benchmark model.

Recent developments in the Internet of Vehicles (IoV) and vehicular adhoc networks (VANET) have revolutionized our infrastructure, making it safer, more convenient, and efficient. VANET provide smart traffic control, event allocation, and real-time information. Existing vehicles in VANET are now equipped with intelligent navigation, entertainment, and emergency applications. However, the highly connected nature of these vehicles poses a significant safety and security risk to drivers and assets which can result in life-threatening consequences. Location privacy is critical, and robust network security techniques should be used to counter threats in VANET environments. Existing schemes like obfuscation, mix-zones, and silent periods have preserved location privacy to some extent but have poor Quality of Service (QoS) and lack both efficiency and security. To address these issues, a shadowing scheme is introduced, which is an improvement of earlier schemes used for location privacy. This approach ensures better service to the vehicle by allowing precise location-based service (LBS) requests to the LBS server and uses blockchain technology for storing vehicular certificates. The inclusion of a group leader significantly reduces the time taken for implementing the scheme, improving efficiency and scalability. The anonymity set size increases over time, offering better privacy protection especially in densely populated areas. The proposed scheme overcomes drawbacks of existing techniques which includes reduced location accuracy and low-quality service in spatial obfuscation techniques, limited applicability and high tracking rate in shadow-based approaches, and reduced utility in distance-based schemes. Moreover, single point of failure and resource-intensive group formation in group-based schemes, and dependency on additional infrastructure in mix-zone-based schemes are also overcome. The proposed scheme’s experimental results validate it, showing that it outperforms current state-of-the-art schemes based on metrics, such as anonymity set size, entropy, and tracking success ratio.

Data publication and sharing are critical components of assessing network infrastructures in the Internet of Things for quality-of-service enhancement. Especially, the advancement in communication technology (e.g., 5G/6G) enables the improvement of the current bottlenecks in the Industrial Internet of Things. Recent approaches remove raw data and their source to achieve a privacy guarantee. However, the data are already anonymized; these still reveal the victim’s extra information using linkage attacks. When data are updated and combined or noise is introduced as part of conventional privacy protection approaches, such as$k$-anonymity, l-diversity, or differential privacy, the usefulness of the released data is diminished, however, posing data utility and computation constraints. In recent years, lightweight privacy-preservation techniques have been proposed for these reasons. However, most of the focus is on syntactic privacy instead of semantic privacy guarantee. Therefore, this article proposes a lightweight semantic privacy-preservation framework for maintaining privacy with high utility efficiency. The proposed paradigm ensures semantic privacy by combining probabilistic random sampling with Instant_Anonymity. Compared to$k$-anonymity, the suggested model demonstrates improved data utility with lower utility errors of 0.00036 and 0.41 for Kullback–Leibler divergence and query error, respectively. The classification accuracy is improved by 0.2%. In addition, the proposed approach is simpler to implement in computation time than the existing state-of-the-art lightweight privacy-preserving strategies.

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.

The recent evolution of the Internet of Things in the healthcare sector has attained substantial recognition from the government and industry. Healthcare data accumulated from diverse sources are stored by health service providers which is useful for patient diagnosis as well as for research for pivotal analysis. However, healthcare data contains sensitive information of an individual that needs to be protected. The sensitive health information of an individual contains multiple attributes and the correlation of this information may lead to a privacy breach. In more complex scenarios, such type of data is expected to be released periodically and dynamically, the privacy breach of individuals becomes imminent due to the presence of personal data. In this article, a fuzzy logic-based intelligent privacy-aware algorithm is proposed to protect the individual’s privacy with multiple sensitive attributes in a dynamic data release scenario. Our formal modeling and analysis show that the proposed approach offers a robust privacy guarantee while releasing health-related data. Furthermore, the empirical evaluation exhibits that the proposed model outperforms the state-of-the-art approaches in terms of information loss and query accuracy.

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.

Data centers (DC) host a large number of servers, computing devices and computing infrastructure, which incur significant electricity / energy. This also results in huge amount of heat produced, which if not addressed can lead to overheating of computing devices in the DC. In addition, temperature mismanagement can lead to thermal imbalance within the DC environment, which may result in the creation of hotspots. The energy consumed during the life of a hotspot is greater than the energy saved during computation. Hence, the thermal imbalance impacts on the efficiency of the cooling mechanism installed inside the DC, which can result in high energy consumption. One popular strategy to minimize energy consumption is to optimize resource allocation within the DC. However, existing scheduling strategies do not consider the ambient effect of the surrounding nodes at the time of job allocation. Moreover, thermal-aware resource scheduling as an optimization problem is a topic that is relatively understudied in the literature. Therefore, in this research, we propose a novel Game-based Thermal-Aware Resource Allocation (GTARA) strategy to reduce the thermal imbalances within the DC. Specifically, we use cooperative game theory with a Nash-bargaining solution concept to model the resource allocation as an optimization problem, where the user jobs are assigned to the computing nodes based on their thermal profiles and their potential effect on the surrounding nodes. This allows us to improve the thermal balance and avoid the hotspots. We then demonstrate the effectiveness of GTARA, TACS, TASA, and FCFS, in terms of minimizing thermal imbalance and the hotspots.

The growing use of electronic health records in the medical domain results in generating a large amount of medical data that is stored in the form of clinical notes. These clinical notes are enriched with clinical entities like disease, treatment, tests, drugs, genes, and proteins. The extraction of clinical entities from clinical notes is a challenging task as clinical notes are written in the form of natural language. The extraction of clinical entities has many useful applications such as clinical notes analysis, medical data privacy, decision support systems, and disease analysis. Although various machine learning and deep learning models are developed to extract clinical entities from clinical notes, developing an accurate model is still challenging. This study presents a novel deep learning-based technique to extract the clinical entities from clinical notes. The proposed model uses local and global context to extract clinical entities in contrast to existing models that use only global context. The combination of CNN, Bi-LSTM, and CRF with non-complex embedding (proposed model) outperforms existing models by a margin of 4-10% and 5-12% in terms of F1-score on i2b2-2010 and i2b2-2012 data. The accurate detection of clinical entities can be helpful in the privacy preservation of medical data that increases the user's and medical organization's trust in sharing medical data.

Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “ c,k -anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.