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

个人隐私保护是发布和共享数据时的首要问题。 该项目寻求更好和更实用的方法来提高所发布的个人层面数据的效用，而不损害个人隐私。差分隐私提供了一个强大的概念，隐私保护的数学术语，而无需假设的背景知识的数据入侵者。尽管在实践中对在发布数据时采用差异隐私有强烈的兴趣，但由于各种原因而存在不情愿，例如，潜在地，高水平的噪声被注入到原始数据中以实现差异隐私，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现差异隐私算法。 该项目开发技术和工具，以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。该项目评估了模拟数据的拟议工作，基于人口普查记录的ADDSPs数据集经常用于匿名化研究，以及帕金森病患者的临床，生物标本和遗传属性的医学数据集，以当前发布不同私人数据的实践为基准。该项目首先建立了理论和方法基础，包括但不限于扩展乘法加权机制以处理非线性查询和数值数据，建立保证发布的替代数据中的个人隐私保护的理论，并着重于实现基于替代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性加权机制的固有属性。 该方法进行评估的模拟研究和应用程序，以现实生活中的数据集（包括社会/金融数据和医疗保健数据）基准对其他方法释放个人层面的数据。最后，正在开发开源软件，以便在Comprehensive R Archive Network和GitHub上发布，该软件生成替代数据集，沿着示例和文档，以解释披露风险和数据分析的支持效用。

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.