计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 315-326.doi: 10.11896/jsjkx.221000053
尹诗玉, 朱友文, 张跃
YIN Shiyu, ZHU Youwen, ZHANG Yue
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。
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