Optimal disclosure risk assessment
最优数据披露风险评估AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究数据隐私与抽样唯一性计数，不对应所列因果、分布效应或金融风险方向。
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最优数据披露风险评估
- 研究问题
- 微观数据发布前，如何估计样本中同时也是总体唯一记录的数量，并确定低抽样比例下非参数估计的可行边界？
- 方法与思路
- 作者在记录数量的泊松丰度模型下提出简单线性估计量，给出统一误差保证，并通过极小极大下界分析估计极限。
- 材料陈述的贡献
- 摘要称在抽样比例低至样本量对数倒数尺度时仍可一致估计，并证明这一尺度的边界及归一化均方误差的近最优性。
- 阅读关注点
- 应核对泊松丰度模型如何对应实际抽样、唯一记录计数的定义，以及抽样比例边界、归一化方式和上下界的精确关系。
- 阅读边界
- 仅据版本未注明的索引摘要；边界结论依赖所述模型，摘要未给出常数和实施细节，估计风险不等于保证数据可安全发布。
原文摘要
Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size n from a finite population of size n¯=n+λn, with λ>0, such that each sample record contains two disjoint types of information: identifying categorical information and sensitive information. Any decision about releasing data is supported by the estimation of measures of disclosure risk, which are defined as discrete functionals of the number of sample records with a unique combination of values of identifying variables. The most common measure is arguably the number τ1 of sample unique records that are population uniques. In this paper, we first study nonparametric estimation of τ1 under the Poisson abundance model for sample records. We introduce a class of linear estimators of τ1 that are simple, computationally efficient and scalable to massive datasets, and we give uniform theoretical guarantees for them. In particular, we show that they provably estimate τ1 all of the way up to the sampling fraction (λ+1)−1∝(logn)−1, with vanishing normalized mean-square error (NMSE) for large n. We then establish a lower bound for the minimax NMSE for the estimation of τ1, which allows us to show that: (i) (λ+1)−1∝(logn)−1 is the smallest possible sampling fraction for consistently estimating τ1; (ii) estimators’ NMSE is near optimal, in the sense of matching the minimax lower bound, for large n. This is the main result of our paper, and it provides a rigorous answer to an open question about the feasibility of nonparametric estimation of τ1 under the Poisson abundance model and for a sampling fraction (λ+1)−1<1/2.