ABSTRACT Privacy‐Preserving Data Sharing (PPDS) masks the individual's collected data (e.g., medical healthcare data) before being disseminated by organizations for analysis and research. Patient data contains sensitive values that must be dealt with while ensuring certain privacy conditions are met. This minimizes the risk of re‐identification of an individual record from the group of privacy‐preserved data. However, with the advancement in technology (i.e., Big Data, the Internet of Things (IoT), and Blockchain), the existing classical privacy‐preserving techniques are becoming obsolete. In this paper, we propose a blockchain‐based secure data sharing technique named “SecureChain”, which preserves the privacy of an individual record using local differential privacy (LDP). The three distinguished features of the proposed approach are lower latency, higher throughput, and improved privacy. The proposed model outperforms the benchmarks in terms of both latency and throughput. In terms of precision, the proposed method improves the accuracy to 88.53% compared to its counterparts, which achieved 49% and 85% accuracy. The results of the experiment verify that the proposed approach outperforms its counterparts.

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.

Vehicular social network (VSN) has been emerged in recent years with the prosperity of Internet of Vehicles (IoV). VSN service providers can exploit the social relationships of vehicles to improve the performance of vehicular networks, such as improving the order-taking efficiency of taxis. However, when social relationships are captured by attackers, other contact information is passively disclosed without their knowledge, seriously threatening user privacy. This article proposes a$(\theta ,\epsilon)$privacy technique for protecting the social relationships of vehicles that can be used to shorten the average order-taking distance of taxis during order allocation. Two variants are included in the proposed technique, which combines graph theory and graph differential privacy (GDP) mechanisms. 1)$(\theta ,\epsilon)$-GDP–a projection-based GDP algorithm is proposed to protect social relationships among the vehicles. 2) Furthermore, an improved order allocation scheme named$(\theta ,\epsilon)$-PrivOT is proposed to reduce the average order-taking distance. Real data evaluations are provided to verify the outperformance of the proposed technique. The privacy level of the proposed algorithm is improved by up to 8.77% while its data utility is improved by up to 22.94% compared with the edge removal scheme. The proposed dispatching scheme can shorten the average order-taking distance by up to 6.89% compared with the scheme of original data with 100 vehicles.

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.