摘要：In the modern era of big data, the vast amount of available data has brought more ways to analyze important economic and financial issues. For example, predicting the probability of individual default has become more accurate, as the number of defaulted individuals has increased year-on-year with the increase in data volume, leading to a more detailed characterization of the defaulted population. However, it presents new challenges and one of them is that all samples are separately stored in different machines and cannot be transferred directly for privacy considerations and limited data storage capacity. This paper develops an improved communication-efficient distributed algorithm in which more local summarized information is used to estimate the high-order derivatives of the loss function with lower communication cost. Furthermore, to protect the privacy in the interacted vector, we design a privacy-preserving algorithm based on the differential privacy constraint by adding a Laplace-distributed noise term in the parameters that can be extended to other cases beyond distributed architectures. Both non-private and private schemes, in which only local estimators are passed from the local machine to the central machine, are more theoretically and practically accurate and efficient than their counterparts. Then we suggest a bootstrap scheme to estimate the covariance matrix of the parametric estimators that is beneficial to effective inference. Finally, we find that the proposed method can effectively handle the practical activities that are, accurate probabilistic predictions of default risk and climate activity.

个人简介：郁淼淼, 华东师范大学副教授，曾任香港科技大学、华东师范大学博士后，研究方向包括大数据分析、隐私保护、质量过程控制。主持国家级项目一项、省部级项目两项，在包括《Journal of Econometrics》、《Journal of Machine Learning Research》、《Annals of Applied Statistics》、《Statistica Sinica》、《Journal of Quality Technology》、《IISE Transactions》等杂志上发表学术论文近二十篇。荣获上海市浦江人才计划、“超级博士后”等荣誉称号。