Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining
作者：
Highlights：
• Two data perturbation methods for privacy-preserving stream mining.
• A novel form of additive noise that accumulates over the course of a stream.
• Known I/O MAP attack variations designed for the proposed perturbation methods.
摘要
•Two data perturbation methods for privacy-preserving stream mining.•A novel form of additive noise that accumulates over the course of a stream.•Known I/O MAP attack variations designed for the proposed perturbation methods.
论文关键词：Data stream mining,Privacy-preserving data mining,Privacy-preserving data publishing,Data perturbation,Maximum A Posteriori attack
论文评审过程：Received 7 September 2019, Revised 30 December 2019, Accepted 11 March 2020, Available online 20 March 2020, Version of Record 10 April 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113380
