文献类型：会议论文

英文题名：Differentially private set-valued data release against incremental updates

作者：Zhang, Xiaojian[1,2]; Meng, Xiaofeng[1]; Chen, Rui[3]

第一作者：Zhang, Xiaojian;张啸剑

机构：[1] School of Information, Renmin University of China, Beijing, China; [2] School of Computer and Information Engineering, Henan University of Economics and Law, China; [3] Department of Computer Science, University of British Columbia, Vancouver, BC, Canada

第一机构：School of Information, Renmin University of China, Beijing, China

会议论文集：Database Systems for Advanced Applications - 18th International Conference, DASFAA 2013, Proceedings

会议日期：April 22, 2013 - April 25, 2013

会议地点：Wuhan, China

语种：英文

外文关键词：Data mining - Database systems - Query processing - Trees (mathematics)

摘要：Publication of the private set-valued data will provide enormous opportunities for counting queries and various data mining tasks. Compared to those previous methods based on partition-based privacy models (e.g., k-anonymity), differential privacy provides strong privacy guarantees against adversaries with arbitrary background knowledge. However, the existing solutions based on differential privacy for data publication are currently limited to static datasets, and do not adequately address today's demand for up-to-date information. In this paper, we address the problem of differentially private set-valued data release on an incremental scenario in which the data need to be transformed are not static. Motivated by this, we propose an efficient algorithm, called IncTDPart, to incrementally generate a series of differentially private releases. The proposed algorithm is based on top-down partitioning model with the help of item-free taxonomy tree and update-bounded mechanism. Extensive experiments on real datasets confirm that our approach maintains high utility and scalability for counting query. ? Springer-Verlag 2013.