Orthogonal array construction and confidentiality in both survey and data-mining contexts

我未来几年的研究兴趣将主要集中在发展自适应统计方法和研究它们的理论性质，以分析大型和复杂的数据集。在复杂调查的背景下，随着调查规模的增加，保护敏感信息不被披露变得越来越具有挑战性。具体的方法可能涉及构建近正交的混合水平阵列，这本身就是现代实验设计领域的一个有趣的研究问题，特别是在设计复杂计算机模拟器的输入设置时，作为更复杂的物理现象的现实替代品，是需要的。同样值得注意的是，在数据挖掘技术被政府机构和企业广泛应用于处理包括敏感信息在内的公共访问数据之后，与上述研究问题密切相关的大型数据仓库的保密性问题引起了人们的极大兴趣。特别是，我打算探索与隐私保护数据挖掘研究领域的算法策略相关的问题，并期望结果对涉及数据挖掘的行业产生相当大的影响。

My research interests for the next few years will be primarily focused on developing adaptive statistical methods and investigating their theoretical properties for the analysis of large and complex datasets.In the context of complex surveys, protecting sensitive information from disclosure has become more and more challenging as the magnitude of such surveys increases. Specific methodologies may involve constructing nearly-orthogonal mixed-level arrays, which by itself is an interesting research problem in the area of modern experimental design, especially when designing input settings for complex computer simulators, as the realistic surrogate to the much more complex physical phenomena, is desired.It is also worth noticing that the issues of maintaining confidentiality in large data warehouses, closely related to the above-mentioned research problem, has drawn significant interests after data-mining techniques have been widely used by government agencies and businesses in processing public access data that also includes sensitive information. In particular, I intend to explore issues relating to algorithmic strategies in the area of privacy-preserving data-mining research and expect the results to be quite influential to industries involved in data-mining.