Social Network Privacy Protection Based on Differential Privacy Technology and Community Discovery Algorithm

Privacy Protection in Cross-System Data Exchange: A Comprehensive Review of Multi-Party Computation Approaches

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.

A flexible privacy-preserving framework for instant messaging in mobile social networks

DPEI: Privacy Budget Savings for Edge Information Protection in Synthetic Graph Publishing

A Serverless Client-Side Privacy Index for Sensitive Data Processing

: The widespread adoption of data-driven systems has intensified concerns regarding the protection of sensitive information and the assessment of privacy risks. Although numerous privacy-preserving techniques and models have been proposed, quantifying and interpreting the level of privacy achieved remains challenging, particularly for non-expert users. This paper introduces the Privacy Index, a unified metric designed to aggregate multiple privacy-related factors into a single, interpretable score. The proposed approach integrates the validation of established privacy models, detection of anonymization techniques, and estimation of re-identification risk under different attacker assumptions. To support practical usage, we develop a serverless, client-side web application that automatically processes structured datasets by classifying attributes and computing the Privacy Index without transmitting data externally. Experimental evaluation demonstrates that the attribute classification component achieves average confidence levels above 88% across multiple datasets, correctly identifying all direct identifiers with high reliability. The system successfully validates privacy models such as k -anonymity and l -diversity and effectively distinguishes between poorly and well-anonymized datasets. The tool is open-source, its code is available at https://github.com/ieeta-mith/DataPrivScore and a demo is available through https://ieeta-mith.github.io/DataPrivScore/.

Privacy-Preserving Data Mining Techniques for Sensitive Datasets

The appraisal of Privacy-Preserving Data Mining (PPDM) has become a crucial research area in current times as a result of the incredible increase in the applications of data-driven applications that use sensitive data (health records, financial transactions, social networks, and governmental databases). Although the data mining techniques have been offering effective tools in the extraction of valuable knowledge, they facilitate great risks to personal privacy when they are applied to sensitive data. Unauthorized disclosure, inference attack, and breach of data has brought up serious ethical, legal, and regulatory issues. As a result, it is difficult to find the compromise between data utility and privacy protection. This essay outlines an extensive analysis of privacy ensuring data mining methods that allow secure privacy of sensitive data without compromising on the analysis accuracy. The paper systematically investigates the ways of anonymization, perturbation, cryptography, and hybrid privacy models. Besides that, newer privacy models include differential privacy, federated learning, and secure multi-party computation are discussed. A systematic approach is given to assess PPDM methods using privacy strength, data utility, computational complexity and scalability. The paper also reports on the findings of the experiments by comparing them, thus showing trade-offs between privacy and performance. The problems, problems under open research and direction are also discovered. The results highlight the lack of universal best practices because no single method is universally the best and the use of PPDM methods should be applied based on the application. The paper is intended to be a reference book of researchers and practitioners looking to have strong privacy preservation solutions such in sensitive data mining tasks.

User behavior prediction model based on big data analysis

A federated learning-based privacy-preserving prediction model is proposed for large-scale e-commerce user behavior analytics. The framework enables multiple e-commerce platforms to collaboratively train predictive models without transmitting raw user data, addressing the dual demand for high accuracy and robust data privacy. The main components of the system include secure aggregation protocols, which protect local model updates from exposure; differential privacy mechanisms, which inject controlled noise during distributed training to obscure individual user contributions. The system architecture supports asynchronous client participation, dynamic scaling, and heterogeneous feature engineering across organizational boundaries, which facilitates deployment in real-world e-commerce environments. Experimental validation conducted in multi-isolated environments shows that the federated method is as accurate as centralized models while significantly reducing privacy leakage. In addition, the model maintains a high recall rate in rare event scenarios and is able to balance privacy budget and prediction performance. Empirical risk assessment indicates that even with an increase in the number of participants or data diversity, privacy protection is significantly enhanced while maintaining analytical utility. The results demonstrate that federated learning is a scalable and effective method that can achieve privacy compliance in e-commerce analytics within data-restricted environments, and it lays a solid foundation for secure distributed business intelligence.