A Differential Privacy Framework with Adjustable Efficiency–Utility Trade-Offs for Data Collection

The widespread use of mobile devices has led to the continuous collection of vast amounts of user-generated data, supporting data-driven decisions across a variety of fields. However, the growing volume of these data raises significant privacy concerns, especially when they include personal information vulnerable to misuse. Differential privacy (DP) has emerged as a prominent solution to these concerns, enabling the collection of user-generated data for data-driven decision-making while protecting user privacy. Despite their strengths, existing DP-based data collection frameworks are often faced with a trade-off between the utility of the data and the computational overhead. To address these challenges, we propose the differentially private fractional coverage model (DPFCM), a DP-based framework that adaptively balances data utility and computational overhead according to the requirements of data-driven decisions. DPFCM introduces two parameters, α and β , which control the fractions of collected data elements and user data, respectively, to ensure both data diversity and representative user coverage. In addition, we propose two probability-based methods for effectively determining the minimum data each user should provide to satisfy the DPFCM requirements. Experimental results on real-world datasets validate the effectiveness of DPFCM, demonstrating its high data utility and computational efficiency, especially for applications requiring real-time decision-making.