AI 中文总结
针对局部模型中单个个体数据的函数隐私发布难题，采用地理隐私（GP）替代标准差分隐私，通过多数据集实验验证了框架的有效性。
AI 中文摘要
我们研究函数的隐私发布问题，尤其关注曲线，曲线是有限区间上连续函数的像。多种数据天然以曲线形式存在，如轨迹数据或一维密度曲线。我们主要关注局部模型场景，其中待私有化的函数捕获单个个体的数据，这一场景更具挑战性，相关前期工作较少。在标准局部差分隐私（DP）概念下，私有化需使任意两个差异极大的函数难以区分，这要求过强，难以保证有效效用；因此我们采用广义的差分隐私概念——地理隐私（GP），该概念允许差异大的函数更易区分，同时对相近函数提供强保护。为验证我们框架的有效性，我们在多个数据集上进行了实验评估。
英文摘要
We study the problem of privately releasing functions, with a particular focus on curves, which are images of continuous functions on some finite interval. Many types of data exist naturally as curves, such as trajectory data or $1$D density curves. We shall primarily be interested in the local model setting, where the function to be privatized captures data belonging to one individual, which is the more challenging setting with limited prior work. Under the standard notion of local differential privacy (DP), any two arbitrarily different functions are required to be made indistinguishable by privatization, which is too strong to allow meaningful utility; we thus work with a generalized notion of DP known as Geo-Privacy (GP), which allows functions far apart to be distinguished more easily while providing strong protection for near functions. To demonstrate the effectiveness of our framework, we provide experimental evaluation on several datasets.
