标题： DP-SNP-TIHMM：用于合成全基因组关联数据集的差分隐私时间非齐次隐马尔可夫模型 显示英文标题

标题： DP-SNP-TIHMM: Differentially Private, Time-Inhomogeneous Hidden Markov Models for Synthesizing Genome-Wide Association Datasets

摘要： 单核苷酸多态性（SNP）数据集对于遗传研究至关重要，但当共享时会带来重大的隐私风险。 SNP之间的相关性使得强大的对抗性攻击，如掩码值重建、亲属关系和成员推断攻击成为可能。 现有的隐私保护方法要么将差分隐私应用于这些数据集的统计摘要，要么提供需要后处理和使用公开可用数据集来抑制或选择性共享SNP的复杂方法。 在本研究中，我们引入了一种创新框架，通过从时变隐马尔可夫模型（TIHMMs）中提取的样本生成合成的SNP序列数据集。 为了保护训练数据的隐私，我们确保每个SNP序列在训练过程中仅产生有限的影响，从而实现强大的差分隐私保证。 关键的是，通过在完整的SNP序列上操作并限制它们的梯度贡献，我们的方法直接解决了其固有相关性带来的隐私风险。 通过在现实世界的1000基因组数据集上进行实验，我们使用隐私预算$\varepsilon \in [1, 10]$在$\delta=10^{-4}$的情况下证明了我们方法的有效性。 值得注意的是，通过允许HMM的转移模型依赖于序列中的位置，我们显著提高了性能，使合成数据集能够紧密复制非隐私数据集的统计特性。 该框架促进了基因组数据的隐私共享，同时为研究人员提供了卓越的灵活性和实用性。 显示英文摘要

摘要： Single nucleotide polymorphism (SNP) datasets are fundamental to genetic studies but pose significant privacy risks when shared. The correlation of SNPs with each other makes strong adversarial attacks such as masked-value reconstruction, kin, and membership inference attacks possible. Existing privacy-preserving approaches either apply differential privacy to statistical summaries of these datasets or offer complex methods that require post-processing and the usage of a publicly available dataset to suppress or selectively share SNPs. In this study, we introduce an innovative framework for generating synthetic SNP sequence datasets using samples derived from time-inhomogeneous hidden Markov models (TIHMMs). To preserve the privacy of the training data, we ensure that each SNP sequence contributes only a bounded influence during training, enabling strong differential privacy guarantees. Crucially, by operating on full SNP sequences and bounding their gradient contributions, our method directly addresses the privacy risks introduced by their inherent correlations. Through experiments conducted on the real-world 1000 Genomes dataset, we demonstrate the efficacy of our method using privacy budgets of $\varepsilon \in [1, 10]$ at $\delta=10^{-4}$. Notably, by allowing the transition models of the HMM to be dependent on the location in the sequence, we significantly enhance performance, enabling the synthetic datasets to closely replicate the statistical properties of non-private datasets. This framework facilitates the private sharing of genomic data while offering researchers exceptional flexibility and utility.