Curriculum Learning and Pattern-Aware Highly Efficient Privacy-Preserving Scheme for Mixed Data Outsourcing with Minimal Utility Loss

A complex problem when outsourcing personal data for public use is balancing privacy protection with utility, and anonymization is a viable solution to address this issue. However, conventional anonymization methods often overlook global information regarding the composition of attributes in data, leading to unnecessary computations and high utility loss. To address these problems, we propose a curriculum learning (CL)-based, pattern-aware privacy-preserving scheme that exploits information about attribute composition in the data to enhance utility and privacy without performing unnecessary computations. The CL approach significantly reduces time overheads by sorting data by complexity, and only the most complex (e.g., privacy-sensitive) parts of the data are processed. Our scheme considers both diversity and similarity when forming clusters to effectively address the privacy–utility trade-off. Our scheme prevents substantial changes in data during generalization by protecting generic portions of the data from futile anonymization, and only a limited amount of data is anonymized through a joint application of differential privacy and k-anonymity. We attain promising results by rigorously testing the proposed scheme on three benchmark datasets. Compared to recent anonymization methods, our scheme reduces time complexity by 74.33%, improves data utility by 19.67% and 68.33% across two evaluation metrics, and enhances privacy protection by 29.19%. Our scheme performs 82.66% fewer lookups in generalization hierarchies than existing anonymization methods. In addition, our scheme is very lightweight and is 1.95× faster than the parallel implementation architectures. Our scheme can effectively solve the trade-off between privacy and utility better than prior works in outsourcing personal data enclosed in tabular form.