| Title |
Differential Privacy-Based Data Collection for Improving Data Utility and Reducing Computational Overhead |
| DOI |
https://doi.org/10.5370/KIEE.2025.74.1.102 |
| Keywords |
Data Privacy; Differential Privacy; Data Utility; Computation Overhead; Data Collection |
| Abstract |
With the rise of real-time data collection through mobile devices such as smartphones, user-driven decision-making systems in various fields such as transportation and healthcare have advanced significantly. However, increasing concerns about the privacy of sensitive information have emerged, leading to the widespread use of differential privacy (DP) to mitigate these risks. Among DP-based data collection methods, local differential privacy (LDP) and distributed differential privacy (DDP) are prominent. While LDP provides low computational overhead, it compromises data utility, while DDP provides better utility at the cost of higher computational overhead. To address these limitations, this paper introduces a DP-compliant (,) data collection method. The proposed approach collects a fraction of of the total items and a fraction of of the total user data, achieving improved data utility compared to LDP while significantly reducing computational overhead compared to DDP, resulting in more efficient data collection. Experimental results on real-world datasets show that the proposed method not only improves data utility over LDP, but also effectively reduces computational overhead over DDP. |