(d, c, l)-Privacy: Privacy Preservation Models for Content-Based Datasets Using Information Retrieval Techniques

The release of datasets containing sensitive user information requires a careful balance between data utility and privacy preservation. To address this challenge, numerous privacy preservation models have been proposed, including k-Anonymity, l-Diversity, t-Closeness, and Differential privacy. However, these models are largely designed for simple datasets in which each attribute is represented by a single (atomic) value, limiting their effectiveness in more complex data environments. Specifically, k-Anonymity and its variants have been widely adopted to mitigate privacy risks arising from quasi-identifier-based inference attacks. While l-Diversity and t-Closeness are extended from k-Anonymity to address the disclosure of sensitive attributes. However, they are primarily effective when sensitive attributes are singular and well defined, which restricts their applicability in scenarios involving complex or content-based data. Another prominent approach is Differential privacy and its variants, which rely on probabilistic mechanisms and the introduction of random noise into query outputs. It provides strong theoretical guarantees and is well suited for numerical data and computation-driven applications. However, it is also less effective for content-based datasets, where semantic meaning and contextual integrity are essential and cannot be preserved through randomization. To overcome these limitations, this study proposes a new privacy preservation model, (d,c,l)-Privacy, specifically designed for content-based datasets. The proposed model ensures that released datasets satisfy the constraints defined by parameters d, c, and l, thereby mitigating potential privacy violations. To enforce these constraints, three algorithms are introduced, i.e., FCFS, greedy, and optimal (d,c,l)-privacy algorithms. The FCFS algorithm prioritizes computational efficiency while maintaining acceptable privacy guarantees. The greedy algorithm balances execution time and data utility. While the optimal algorithm focuses on maximizing semantic preservation and overall data usefulness, albeit at a higher computational cost. Experimental results show that the proposed algorithms effectively mitigate privacy risks in released datasets under (d,c,l)-privacy constraints. Among the evaluated algorithms, FCFS achieves the highest computational efficiency, while the greedy algorithm provides a favorable trade-off between efficiency and data utility. The optimal algorithm consistently delivers the highest level of data quality, despite increased computational overhead. These findings indicate that the proposed model and algorithms provide an effective and practical solution for privacy preservation data publishing in real-world, content-based data environments.