Balancing the privacy-utility trade-off: How to draw reliable conclusions from private data
This work addresses the challenge non-technical users face in understanding the trade-off between privacy loss parameters and the reliability of statistical inference in differential privacy. By reframing the privacy–utility trade-off within a hypothesis testing framework, the paper introduces the concept of “relative disclosure risk,” which directly links privacy loss parameters to the validity of statistical inference. Through theoretical analysis of how differentially private mechanisms affect the significance of hypothesis tests, the study quantifies the uncertainty introduced by randomization and its effectiveness in mitigating membership inference attacks. The resulting insights yield an actionable guideline for non-expert users to select appropriate privacy mechanisms, thereby promoting transparent deployment and principled configuration of differential privacy in practical applications.