Jul 2026· Journal of Data, Information and Management· Vol 8, pp. 171 - 191· 0 citations· 50 references
TL;DR
The results demonstrate that SA-LDP-DW supports responsible data sharing, data governance, and privacy-aware data analytics, enabling privacy protection, ownership verification, and reliable analytical utility for real-world data-driven applications.
In today’s era of big data, personal privacy is increasingly at risk due to widespread data sharing. Mobile applications often collect excessive personal information, while advanced analytics can sometimes lead to biased or discriminatory practices. These challenges create an urgent need for secure, privacy-preserving methods that allow sensitive data to be shared and analyzed across multiple parties and diverse systems. This paper reviews the progress made in this area, with a particular focus on the requirements for safe data sharing and controlled dissemination of private information during multi-party data fusion. The review is structured around three main perspectives: privacy-preserving computation, information sharing control, and collaborative secure computation. We begin by examining the current state of privacy protection in large-scale, interconnected environments, followed by a comparison of recent research developments at both national and international levels. In the area of privacy-preserving computation, emerging techniques such as full-lifecycle privacy safeguards, information flow control, and secure data exchange mechanisms are discussed. For information sharing control, three approaches are analyzed—local control, extended control, and desensitization methods. In collaborative secure computation, we outline methods currently being applied in both academic and industry contexts. Finally, the paper highlights key challenges and directions for future research. Traditional approaches such as anonymization, perturbation, and access control, as well as more advanced methods like cryptography and federated learning, all face practical limitations. To achieve robust protection throughout the entire data lifecycle, theoretical models and privacy-aware information systems must be further refined and tailored to different real-world application scenarios.
Chaitanya Tumma, Supraja Ayyamgari, Charan Thumma et al.· 2026 International Conferenc...· 0 citations
The experiment showed that Autoencoder Based Federated Learning was a scalable, secure, and privacy-efficient solution to applications tailored for healthcare, finance, and other sensitive data environments.
Guman Singh Chauhan, venkata Surya Teja Gollapalli, Kannan Srinivasan et al.· Journal of Science & Technol...· 0 citations
In the digital era, personal data privacy protection has become a key element of sustainable development. However, most existing literature examines technical, legal, and behavioural aspects in isolation, overlooking the nonlinear propagation mechanisms across different levels. This study integrates a literature review and expert interviews to comprehensively survey existing works and identifies thirteen determinants involving data subjects, data collectors, and regulatory agencies. Building on this foundation, we innovatively integrate a three-stage hybrid methodology—DEMATEL-ISM-MICMAC—to systematically map causal dependencies, hierarchical structures, and attribute robustness among factors. The results reveal that the completeness of legal frameworks and enforcement intensity function as core drivers, whereas trust constitutes a highly dependent yet fragile node. Critical transmission paths encompass the longest chain of “institution–enforcement–cognition–trust” and the shortest chain of “legal provisions→processing transparency”. On this basis, we propose a five-dimensional policy package that (i) reinforces regulatory drivers, (ii) institutionalises trust-repair mechanisms, (iii) leverages trauma-induced learning effects, (iv) optimises transmission pathways, and (v) implements full-cycle collaborative governance, thereby offering a systemic intervention framework to resolve the dilemma of fragmented governance.
Ting Zhang, Zhong-Xian Duan· Humanities and Social Scienc...· 0 citations
The findings indicate that unifying adaptive privacy preservation with decentralized integrity auditing yields a more complete cloud-security posture than either mechanism alone, and the paper outlines the empirical validation, including full-scale testbed experiments, required before deployment.
Jayakumar D, M. Ramamoorthy· International journal of com...· 0 citations
A privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics and incorporates privacy-enhancing technologies to ensure confidentiality, integrity, and transparency is presented.
Karen Spärck Jones, Donald Michie· International Journal of Dat...· 0 citations
The Privacy Index is introduced, a unified metric designed to aggregate multiple privacy-related factors into a single, interpretable score that integrates the validation of established privacy models, detection of anonymization techniques, and estimation of re-identification risk under different attacker assumptions.
José A. Gameiro, J. Oliveira, João Rafael Almeida· International Conference on...· 0 citations
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