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
Today's digital age has made privacy and data protection a major concern-generally, with the kind of technologies that are turning things around and bringing everything to the cloud. FL will most likely provide a solution to the distance and make things clear in collaboration without exposing raw information from a consortium to boost its privacy. However, existing FL solutions include such challenges as increased overhead communication, risk in leaking data, and even the inefficiency of secure aggregation.To mitigate these constraints, this research proposes the Autoencoder-Based Federated Learning framework by integrating prevailing techniques such as differential privacy and homomorphic encryption that safeguard both the security and efficiency of the model. This method does not only steal model ideas for autoencoders to compress before sciences transmission but hugely reduces the transmission bandwidth and possibly minimizes gradient leakage. However, adaptive normalization is used to handle institutional heterogeneity to maintain better performance for the model. Conclusion of experimentation indicated that this framework could significantly reduce communication overhead while retaining high federated learning accuracy and even better security. Further, the trust-based client evaluation mechanism is presented to detect malicious behavior and improve reliability regarding federated aggregation. 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
The growing adoption of data-driven decision-making has improved operational intelligence, predictive analytics, and strategic planning across enterprises. However, privacy regulations, data sovereignty requirements, and competitive concerns often restrict direct data sharing between organizations. Federated Data Engineering (FDE) addresses these challenges by enabling collaborative analytics without transferring sensitive raw data. This paper presents a privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics. The framework supports decentralized data preprocessing, feature engineering, and encrypted parameter sharing while complying with regulations such as GDPR and HIPAA. It incorporates privacy-enhancing technologies, including differential privacy, secure multi-party computation, homomorphic encryption, and blockchain-based auditing, to ensure confidentiality, integrity, and transparency. The proposed architecture also improves scalability, communication efficiency, fault tolerance, and interoperability across heterogeneous enterprise environments. Experimental evaluation demonstrates enhanced collaborative analytics with reduced privacy risks and communication overhead. The framework provides a practical foundation for secure, trustworthy, and privacy-preserving cross-enterprise data collaboration in healthcare, finance, manufacturing, and other data-intensive industries.
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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