Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

• Main Authors: Yuan, Ganzhao, Zhang, Zhenjie, Winslett, Marianne, Xiao, Xiaokui, Yang, Yin, Hao, Zhifeng
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2017
• Subjects: Linear counting query; Differential privacy
• Online Access: https://hdl.handle.net/10356/81387; http://hdl.handle.net/10220/43472
• Institution: 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...