2026· Proceedings of the 15th International Conference on Data Science, Technology and Applications· 0 citations· 24 references
Abstract
: 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/.
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.
B. S. Shah· International Journal of App...· 0 citations
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 rapid growth of omni-commerce applications has led to increased reliance on artificial intelligence systems for personalised services, demand forecasting, and customer behaviour analysis. However, a huge amount of user information that is gathered and processed during this process leads to serious privacy and security issues, especially in the context of logging systems where sensitive data is stored. The proposed paper is a Privacy-Aware AI Logging Framework that is aimed at providing secure, compliant and efficient management of log data within omni-commerce settings. A huge amount of user information that is gathered and processed during this process leads to serious privacy and security issues, especially in the context of logging systems where sensitive data is stored. The proposed system adopts a modular architecture that supports real-time data processing while minimising privacy risks without compromising system performance. Experimental analysis shows that the framework has a good balance between data utility and privacy protection, as it has a high level of accuracy in data analytics and minimal exposure to sensitive data. This approach enhances trust in AI-driven omni-commerce platforms and provides a scalable solution for privacy-aware data management in modern digital ecosystems.
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.
In autonomous data exchange environments, where data is flowing in real-time, it is crucial to have comprehensive security solutions to ensure user privacy and system performance. This paper presents a Dynamic Privacy Preservation Architecture specifically developed for untrusted, decentralised networks of autonomous nodes that frequently interact. The framework uses adaptive anonymisation algorithms and decentralised trust verification mechanisms to dynamically protect private data tokens based on context sensitivity and the recipient's risk profile. The evaluation of this architecture was conducted through an experimental study using a synthetic dataset from the operational Internet of Things network, comprising 159 distinct communications. Using the Python programming language and Python-specific data science packages such as Pandas for data manipulation, Scikit-learn for metric evaluation, and Matplotlib for visual plotting, system performance, computational overhead and privacy metrics were simulated and analysed. The results show that the proposed architecture is suitable to optimise the data utility-privacy trade-off in absolute terms while preserving strict privacy. The system dynamically scales its defence mechanisms to keep processing latencies low and effectively prevent unauthorised reconstruction attacks, thereby providing a reliable, secure and highly scalable ecosystem for autonomous data stakeholders.
Chaitanya Bharath Somineni· FMDB Transactions on Sustain...· 0 citations
Privacy preservation and data utility are two important key factors for publishing the data of multiple sensitive
attributes. Traditional anonymization methods often fail to protect against re-identification risks in complex datasets. The
proposed system uses semantic L-diversity technique to partition the data into the different buckets. Slicing technique is used to
partition the data into multiple sensitive tables along with the quasi table. This approach also uses a bucket id to keep the
associations among the sensitive tables and quasi table. Additionally, the framework provides a mechanism to generate and
securely re-identify summary tables derived from sensitive datasets, ensuring a balance between accessibility and confidentiality.
The system proposes a framework that ensures data utility while minimizing privacy risks. The system aims to provide a solution
for organizations to publish valuable data responsibly without compromising individual privacy
Bonumaddi Kumari, Chikkala Soujanya· International Journal for Re...· 0 citations