Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

• Main Authors: Yuan, Ganzhao, Zhang, Zhenjie, Winslett, Marianne, Xiao, Xiaokui, Yang, Yin, Hao, Zhifeng
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2017
• 主題: Linear counting query; Differential privacy
• 在線閱讀: https://hdl.handle.net/10356/81387; http://hdl.handle.net/10220/43472
• 機構: Nanyang Technological University

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published...