...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Inference Attacks and Controls on Genotypes and Phenotypes for Individual Genomic Data
【24h】

Inference Attacks and Controls on Genotypes and Phenotypes for Individual Genomic Data

机译：推论攻击和对个体基因组数据基因型和表型的控制

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The rapid growth of DNA-sequencing technologies motivates more personalized and predictive genetic-oriented services, which further attract individuals to increasingly release their genome information to learn about personalized medicines, disease predispositions, genetic compatibilities, etc. Individual genome information is notoriously privacy-sensitive and highly associated with relatives. In this paper, we present an inference attack algorithm to predict target genotypes and phenotypes based on belief propagation in factor graphs. With this algorithm, an attacker can effectively predict the target genotypes and phenotypes of target individuals based on genome information shared by individuals or their relatives, and genotype and phenotype association from genome-wide association study (GWAS). To address the privacy threats resulted from such inference attacks, we elaborate the metrics to evaluate data utility and privacy and then present a data sanitization method. We evaluate our inference attack algorithm and data sanitization method on real GWAS dataset: Age-related macular degeneration (AMD) case/control dataset. The evaluation results show that our work can effectively defense against genome threats while guaranteeing data utility.
机译：DNA排序技术的快速生长激励更个性化和预测的遗传遗传服务，进一步吸引个人越来越释放他们的基因组信息，以了解个性化药物，疾病易感性，遗传融合等。个体基因组信息是众所周知的隐私敏感和亲戚高度相关。在本文中，我们提出了一种推论攻击算法，以预测基于因子图中的信仰传播的目标基因型和表型。利用该算法，攻击者可以基于各自或其亲属共享的基因组信息有效地预测靶个体的目标基因型和表型，以及来自基因组 - 宽协会研究（GWAS）的基因型和表型关联。为了解决这些推理攻击所产生的隐私威胁，我们详细说明了指标来评估数据实用程序和隐私，然后呈现数据消毒方法。我们评估我们的推理攻击算法和Real GWAS数据集的数据消毒方法：年龄相关的黄斑变性（AMD）案例/控制数据集。评估结果表明，我们的工作可以有效地防御基因组威胁，同时保证数据效用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号