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Differential privacy under dependent tuples-the case of genomic privacy

机译：依赖元组下的差异隐私 - 基因组隐私的情况

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Motivation: The rapid progress in genome sequencing has led to high availability of genomic data. Studying these data can greatly help answer the key questions about disease associations and our evolution. However, due to growing privacy concerns about the sensitive information of participants, accessing key results and data of genomic studies (such as genome-wide association studies) is restricted to only trusted individuals. On the other hand, paving the way to biomedical breakthroughs and discoveries requires granting open access to genomic datasets. Privacy-preserving mechanisms can be a solution for granting wider access to such data while protecting their owners. In particular, there has been growing interest in applying the concept of differential privacy (DP) while sharing summary statistics about genomic data. DP provides a mathematically rigorous approach to prevent the risk of membership inference while sharing statistical information about a dataset. However, DP does not consider the dependence between tuples in the dataset, which may degrade the privacy guarantees offered by the DP.
机译：动机：基因组测序的快速进展导致了基因组数据的高可用性。研究这些数据可以极大地帮助回答有关疾病协会和进化的关键问题。然而，由于对参与者的敏感信息的敏感信息的日益增长的担忧，访问基因组研究的关键结果和数据（例如基因组关联研究）仅限于仅信任的个人。另一方面，向生物医学突破和发现铺平途径需要授予对基因组数据集的开放访问。保留保护机制可以是在保护其所有者的同时授予更广泛的此类数据的解决方案。特别是，在分享关于基因组数据的汇总统计时，对应用差异隐私（DP）的概念越来越感兴趣。 DP提供了一种数学上严格的方法来防止隶属隶属推断的风险，同时共享关于数据集的统计信息。但是，DP不考虑数据集中元组之间的依赖，这可能会降低DP提供的隐私保障。

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