基于属性隐私的统计查询定价模型OA北大核心CSTPCD
Pricing statistical query based on attribute privacy
现有统计查询定价模型没有考虑查询结果揭露数据集敏感属性的问题,难以通过相应地补偿数据提供方激励共享,对此提出一种基于属性隐私的定价模型.首先,基于提出的宽松近似Wasserstein机制(RAWM)计算查询敏感度,直接计算输出分布对距离的宽松上界以提高效率;然后,以约束属性隐私损失为前提,根据查询敏感度、噪声方差、补偿参数对数据提供方进行补偿;最后,在补偿之上运用成本加成法设计了多个无套利定价函数,可以针对单补偿成本和多边际成本等场景定价.实验结果表明,查询敏感度的计算时间从线性复杂度降低到了常数复杂度,在一亿数据量下仅有0.52%的效用代价;定价模型能够提供细粒度补偿以激励共享;设计的定价函数满足无套利性.
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.
方嘉豪;郭兵
四川大学计算机学院,成都 610065
计算机与自动化
数据定价数据共享属性隐私河豚鱼隐私无套利
data pricingdata sharingattribute privacypufferfish privacyarbitrage freeness
《计算机应用研究》 2024 (010)
2978-2986 / 9
国家自然科学基金铁路基础研究联合基金资助项目(U2268204)
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