Abstract

Objective

Differential privacy is a relatively new method for data privacy that has seen growing use due its strong protections that rely on added noise. This study assesses the extent of its awareness, development, and usage in health research.

Materials and Methods

A scoping review was conducted by searching for [“differential privacy” AND “health”] in major health science databases, with additional articles obtained via expert consultation. Relevant articles were classified according to subject area and focus.

Results

A total of 54 articles met the inclusion criteria. Nine articles provided descriptive overviews, 31 focused on algorithm development, 9 presented novel data sharing systems, and 8 discussed appraisals of the privacy-utility tradeoff. The most common areas of health research where differential privacy has been discussed are genomics, neuroimaging studies, and health surveillance with personal devices. Algorithms were most commonly developed for the purposes of data release and predictive modeling. Studies on privacy-utility appraisals have considered economic cost-benefit analysis, low-utility situations, personal attitudes toward sharing health data, and mathematical interpretations of privacy risk.

Discussion

Differential privacy remains at an early stage of development for applications in health research, and accounts of real-world implementations are scant. There are few algorithms for explanatory modeling and statistical inference, particularly with correlated data. Furthermore, diminished accuracy in small datasets is problematic. Some encouraging work has been done on decision making with regard to epsilon. The dissemination of future case studies can inform successful appraisals of privacy and utility.

Conclusions

More development, case studies, and evaluations are needed before differential privacy can see widespread use in health research.

中文翻译：

---

健康研究中的差异隐私：范围审查

摘要

客观的

差分隐私是一种相对较新的数据隐私方法，由于其强大的保护依赖于额外的噪音，因此得到了越来越多的使用。本研究评估了其在健康研究中的认识、发展和使用程度。

材料和方法

通过在主要健康科学数据库中搜索[“差异隐私”和“健康”]进行范围审查，并通过专家咨询获得了额外的文章。相关文章根据学科领域和重点进行分类。

结果

共有54篇文章符合纳入标准。9 篇文章提供了描述性概述，31 篇侧重于算法开发，9 篇介绍了新颖的数据共享系统，8 篇讨论了对隐私-效用权衡的评估。讨论差异隐私的最常见的健康研究领域是基因组学、神经影像学研究和使用个人设备的健康监测。算法最常被开发用于数据发布和预测建模。关于隐私效用评估的研究考虑了经济成本效益分析、低效用情况、个人对共享健康数据的态度以及隐私风险的数学解释。

讨论

差分隐私在健康研究中的应用仍处于开发的早期阶段，并且对现实世界实施的描述很少。很少有用于解释性建模和统计推断的算法，特别是对于相关数据。此外，小数据集的准确性降低是有问题的。在有关 epsilon 的决策制定方面已经做了一些令人鼓舞的工作。未来案例研究的传播可以为成功评估隐私和效用提供信息。

结论

在差异隐私可以在健康研究中得到广泛使用之前，需要进行更多的开发、案例研究和评估。