计算机应用研究2024，Vol.41Issue(10)：2978-2986,9.DOI:10.19734/j.issn.1001-3695.2024.02.0044
基于属性隐私的统计查询定价模型
Pricing statistical query based on attribute privacy
摘要
Abstract
Current statistical query pricing models have not considered the problem that query results reveal sensitive attri-butes of datasets,making it difficult to incentivize sharing by compensating data providers accordingly.Therefore,this paper proposed a pricing model based on attribute privacy.Firstly,the model calculated query sensitivity based on the relaxed ap-proximation Wasserstein mechanism(RAWM)proposed,improving efficiency by directly calculate the relaxed upper bound of output distribution pairs.Then,with bounding privacy loss,the model compensated data providers based on query sensitivity,noise variance and compensation parameters.Finally,by using cost-plus pricing on compensation,this paper designed several arbitrage-free pricing functions,which could be used in scenarios such as single compensation costs and multiple marginal costs.The experiment results show that the running time of calculating query sensitivity is reduced from linear complexity to con-stant complexity,with a utility cost of only 0.52％when data volume is 100 million.The pricing model provides fine-grained compensation to incentivize sharing.Pricing functions satisfy arbitrage freeness.关键词
数据定价/数据共享/属性隐私/河豚鱼隐私/无套利Key words
data pricing/data sharing/attribute privacy/pufferfish privacy/arbitrage freeness分类
信息技术与安全科学引用本文复制引用
方嘉豪,郭兵..基于属性隐私的统计查询定价模型[J].计算机应用研究,2024,41(10):2978-2986,9.基金项目
国家自然科学基金铁路基础研究联合基金资助项目(U2268204) （U2268204）
