Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 356-362· 0 citations· 16 references
Abstract
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.
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.
Omar Almomani, K. L. Raghavender Reddy, Vikram V. Patel et al.· Journal of Data, Information...· 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
Now, AI runs on cloud platforms, edge systems with federated settings, and in large language model (LLM) pipelines or data-sharing services, creating even wider privacy leakage paths beyond classical database disclosure. This paper offers a systematic, structured review of the literature on a curated, cost-effective reference corpus for quantifying and preventing privacy leakage in AI-enabled data ecosystems. The review ties together four strands of research that are often treated separately. Firstly, the privacy risk throughout the AI life cycle. Secondly, the measurement of the quantitative leakage. Thirdly, architectures of the privacy-preserving models, and finally, operational governance for real-world deployment. Our analysis demonstrates that state-of-the-art approaches are moving from static mechanisms based on anonymization to metric-aware protections, including information-theoretic leakage scores, cumulative differential privacy accounting, personalized privacy budgets, and benchmark-driven attack evaluation. In parallel, prevention methods are evolving beyond single homomorphic noise injection and are becoming multi-layered defenses that combine differential privacy, federated learning, weight quantization, synthetic data generation, policy-driven automation, and LLM controls. The review uncovers four itchy gaps: fractured assessment metrics, shaky privacy-utility trade-offs, flimsy integration of technological controls and compliance processes, and low cross-context validation across cloud-based computing, edge computing (data processing at or near the source), federated learning (distributed machine-learning methods), and generative AI systems. The paper concludes by outlining a unified research agenda to build AI-aware, quantifiable, and usable privacy protection stacks.
A privacy-preserving, secure data-sharing framework tailored for edge-cloud collaborative architectures that minimizes the computational overhead on the terminal side while safeguarding user privacy, and effectively reduces the overhead associated with user joining and revocation within the same group.
Qikun Zhang, Zheng Cai, Jinbo Feng et al.· Journal of King Saud Univers...· 0 citations
This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination, and identifies promising directions for future research.
Shivendra Shukla, C. S. Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
The proposed framework combines adaptive differential privacy optimization and the trust-aware collaboration management to enable secure and reliable information sharing and achieves better privacy protection, less communication overhead and better collaborative intelligence performance than traditional privacy-aware exchange mechanisms.
