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

在发布和共享数据时，保护个人隐私是首要关注的问题。该项目寻求更好和更实用的方法，在不损害个人隐私的情况下，提高已发布的个人级别数据的效用。差异隐私在数学上为隐私保护提供了一个可靠的概念，而不需要对数据入侵者的背景知识做出假设。尽管在实践中对在发布数据时采用差异隐私有着浓厚的兴趣，但由于各种原因--例如，可能向原始数据注入高水平的噪声以实现差异隐私，特别是在高维数据中；以及缺乏用户友好的软件和工具来在实践中实施差异隐私算法--存在着不情愿的情况。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“代理数据集”，满足不同的隐私，同时为有效和准确的人口级统计分析提供足够的信息。该项目使用模拟数据、经常在匿名化研究中使用的基于普查记录的成人数据集，以及具有帕金森患者的临床、生物显微镜和遗传属性的医疗数据集来评估拟议的工作，并以当前发布不同私人数据的做法为基准。该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字数据，建立了在发布的代理数据中保证个人隐私保护的理论，并专注于实现基于代理数据的推理的统计有效性。降低必要的噪声水平以实现不同的隐私利用了最先进的降维技术和乘法加权机制的固有属性。该方法是通过模拟研究和对真实生活数据集(包括社会/金融数据和医疗保健数据)的应用来评估的，并以其他发布个人级别数据的方法为基准。最后，正在开发开放源码软件，以便在综合R档案网络和GitHub上发布，该软件产生替代数据集，以及解释披露风险和数据分析的支持效用的例子和文件。

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.