SaTC: CORE: Small: Differentially Private Data Synthesis via Muji: Multiplicative Weights Update via Jackknifed Influence

在发布和共享数据时，保护个人隐私是最重要的问题。该项目寻求更好和更实际的方法，在不损害个人隐私的情况下增强已发布的个人数据的效用。差分隐私在不假设数据入侵者的背景知识的情况下，用数学术语为隐私保护提供了一个健壮的概念。尽管在实践中对发布数据时采用差分隐私非常感兴趣，但由于各种原因存在不情愿，例如，在原始数据中可能注入高水平的噪声以实现差分隐私，特别是在高维数据中；而在实际应用中缺乏用户友好的软件和工具来实现差分私有算法。该项目开发技术和工具来创建与原始数据具有相同结构的合成“替代数据集”，满足差异隐私，同时为有效和准确的人口统计分析提供足够的信息。该项目使用模拟数据、匿名化研究中经常使用的基于人口普查记录的ADULT数据集，以及包含帕金森患者临床、生物标本和遗传属性的医学数据集来评估拟议的工作，并以当前发布不同私人数据的实践为基准。这项工作正在几个社区外展项目中展出，以激发K-12学生对STEM职业的兴趣。项目首先建立了理论和方法基础，包括但不限于将乘法加权机制扩展到处理非线性查询和数值数据，建立了在发布的代理数据中保证个人隐私保护的理论，并着重实现基于代理数据的推断的统计有效性。降低必要的噪声水平以实现差分隐私，利用了最先进的降维技术和乘法加权机制的固有特性。通过模拟研究和对现实生活数据集（包括社会/金融数据和医疗保健数据）的应用，对该方法进行评估，并以发布个人数据的其他方法为基准。最后，正在开发的开源软件将在综合R存档网络和GitHub上发布，该软件可以生成替代数据集，以及解释披露风险和数据分析支持的实用程序的示例和文档。

Protection of individual privacy is a top concern when releasing and sharing data. This project seeks better and more practical ways to enhance the utility of released individual-level data without compromising individual privacy. Differential privacy provides a robust concept for privacy protection in mathematical terms without making assumptions about the background knowledge of data intruders. Despite a strong interest in practice to adopt differential privacy when releasing data, reluctance exists because of various reasons - e.g., potentially a high level of noise injected into the original data to achieve differential privacy, especially in high-dimensional data; and the lack of user-friendly software and tools to implement differentially private algorithms in practice. This project develops techniques and tools to create synthetic "surrogate datasets" with the same structure as the original data, satisfying differential privacy while offering sufficient information for valid and accurate population-level statistical analysis. The project evaluates the proposed work with simulated data, the census-record-based ADULT dataset frequently used in anonymization studies, and a medical dataset with clinical, biospecimen, and genetic attributes from Parkinson's patients, benchmarked against current practice for releasing different private data. The work is being featured in several community outreach programs to stimulate interests in STEM careers among K-12 students.The project first establishes theoretical and methodological foundations, including but not limited to extending the multiplicative weighting mechanism to handle nonlinear queries and numerical data, establishing a theory that guarantees individual privacy protection in the released surrogate data, and focusing on achieving the statistical validity of inferences based on the surrogate data. The reduction of the necessary noise level to achieve differential privacy leverages the state-of-the-art dimensional reduction techniques and the inherent properties of the multiplicative weighting mechanism. The method is evaluated by simulation studies and applications to real-life datasets (including social/financial data and health care data) benchmarked against other methodologies for releasing individual-level data. Finally, open-source software is being developed for release on the Comprehensive R Archive Network and GitHub that produces surrogate datasets, along with examples and documents to explain the disclosure risk and the supported utility of data analysis.