We study the problem of performing counting queries at different levels in hierarchical structures while preserving individuals' privacy. We propose a new error measure for this setting by considering a combination of multiplicative and additive approximation to the query results. We examine known mechanisms in differential privacy (DP) and prove their optimality in the pure-DP setting. In the approximate-DP setting, we design new algorithms achieving significant improvement over known ones.
翻译:我们研究在维护个人隐私的同时,在等级结构的不同层次进行计数的问题,我们建议对这一环境采取新的错误措施,考虑与查询结果相结合的倍增效应和添加近似值。我们研究了不同隐私(DP)的已知机制,并证明它们在纯DP环境中是最佳的。在大约的DP环境中,我们设计新的算法,比已知的算法有显著改进。