摘要
相对于传统的中心化差分隐私,本地差分隐私(Local Differential Privacy,LDP)具有不依赖可信第三方等优势,但也存在数据效用较低的问题。效用优化本地差分隐私模型ULDP(Utility-Optimized Local Differential Privacy)利用不同输入的敏感度差异,可以提升估计结果的准确度。二维数据联合分布计算可广泛应用于众多数据分析场景,然而,如何在ULDP模型下实现二维数据联合分布估计,仍然是尚未解决的重要问题。针对这一问题,首先给出了二维ULDP模型的定义,兼顾了两个属性分别敏感与否的4种情况;其次,在该模型下,针对联合分布估计问题,提出了JuRR(Joint Utility-Optimized Randomized Response)与CPRR(Cartesian Product Randomized Response)2种机制,并理论证明了估计结果的无偏性;最后,在真实数据集上进行对比实验,讨论了不同参数对估计误差的影响。实验结果表明,所提2种机制具有更高的数据效用。
Compared with traditional centralized differential privacy,local differential privacy(LDP)has the advantage of not relying on trusted third parties,but it also has the problem of low data utility.The utility-optimized local differential privacy(ULDP)can improve the accuracy of estimation results by taking advantage of the sensitivity differences of different inputs.Two-dimensional data joint distribution calculation can be widely used in many data analysis scenarios.However,how to realize two-dimensional data joint distribution estimation under the ULDP model is still an important problem that has not yet been solved.Aiming at this problem,the definition of the two-dimensional ULDP model is given first,taking into account the four cases of whether the two attributes are sensitive or not.Secondly,under this model,for the joint distribution estimation problem,two mechanisms joint utility-optimized randomized response(JuRR)and cartesian product randomized response(CPRR)are proposed,and the unbiasedness of the estimation results is proved theoretically.Finally,comparative experiments are carried out on real datasets to discuss the influence of different parameters on the estimation error.Experimental results show that the proposed two mechanisms have better data utility.
作者
尹诗玉
朱友文
张跃
YIN Shiyu;ZHU Youwen;ZHANG Yue(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《计算机科学》
CSCD
北大核心
2023年第10期315-326,共12页
Computer Science
基金
国家重点研发计划(2020YFB1005900)
国家自然科学基金(62172216)
江苏省自然科学基金(BK20211180)
广西密码学与信息安全重点实验室研究课题(GCIS202107)。
关键词
本地差分隐私
效用优化
联合分布
频率估计
敏感度差异
Local differential privacy
Utility-optimized
Joint distribution
Frequency estimation
Difference in sensitivity
