To address the issues of existing medical data collection algorithms being unable to effectively resist background know-ledge attacks and the lack of a trusted third party leading to privacy breaches,a medical data collection method was proposed.A two-stage data collection framework based on the Count-Min Sketch technique and GRR algorithm was utilized.Random sam-pling techniques were employed to avoid privacy budget fragmentation,communication costs and noise errors were reduced.High and low frequency symptoms were separately sampled and perturbed to mitigate errors caused by hash collision.Theoretical anal-ysis indicates that the proposed method meets the local differential privacy requirement.Experimental results indicate that the algorithm outperforms the comparative methods in both frequency estimation accuracy,runtime consumption and communication expense.
关键词
医疗数据收集/本地差分隐私/草图结构/分层收集/不可信第三方/隐私保护/数据可用性
Key words
medical data collection/local differential privacy/count-min sketch/hierarchical data collection/untrusted third party/privacy protection/data utility
维普
万方数据