Utility-preserving differentially private skyline query
作者：
Highlights：
• A novel algorithm to address privacy risk in skyline query is proposed.
• Concerns on the reduced data utility of differential privacy are solved.
• Sensitivity can be further reduced by transforming the basis of local sensitivity.
• Our algorithm can quantify the level of privacy protection for skyline query.
• Our algorithm has obvious advantages when the privacy budget is small.
摘要
•A novel algorithm to address privacy risk in skyline query is proposed.•Concerns on the reduced data utility of differential privacy are solved.•Sensitivity can be further reduced by transforming the basis of local sensitivity.•Our algorithm can quantify the level of privacy protection for skyline query.•Our algorithm has obvious advantages when the privacy budget is small.
论文关键词：Privacy preserving,Differential privacy,Skyline query,Spectral clustering
论文评审过程：Received 22 July 2020, Revised 26 July 2021, Accepted 3 September 2021, Available online 20 September 2021, Version of Record 24 September 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115871
