Local disclosure control techniques of statistical microdata sets

这一年，我们在统计微数据集披露控制技术方面取得了一些重要成果。在国际统计学会的会议上，Takemura就“用列联表单元概率相互作用的加性建模来评估每条记录的识别风险”这篇论文做了特邀演讲。这篇论文将被列入由国际调查统计学家协会出版的一份单独的论文集。Nobuaki Hoshino的论文《Pitman抽样公式在微数据披露风险评估中的应用》发表在《官方统计杂志》上。作为一项新的研究成果，卡内基梅隆大学发表了两篇讨论论文《关于条件反高斯-泊松分布的随机聚类》和《关于由条件反高斯-泊松分布导出的限制随机分区结构》。

We obtained several important results on the disclosure control techniques of statistical microdata sets in this year.Takemura gave an invited talk on the paper "Evaluation of per-record identification risk by additive modeling of interaction for contingency table cell probabilities" at the meeting of International Statistical Institute. This paper will be included in a separate proceedings published by International Association of Survey Statisticians. A paper "Minimum unsafe and maximum safe sets of variables for disclosure risk assessment of individual records in a microdata set" will be published in Journal of Japan Statistical Society The paper "On application of Pitman's sampling formula to microdata disclosure risk assessment" by Nobuaki Hoshino was published in Journal of Official Statistics. As a new research results, two discussion papers "On the random clustering with the conditional inverse Gaussian-Poisson distribution" and "On limiting random partition structure derived from the conditional inverse Gaussian-Poisson distribution" are published as discussion paper in Carnegie Mellon university.