With the continuous exploration of genetic research, gradually exposed privacy issues become the bottleneck that limits its development. DNA motif finding is an important study to understand the regulation of gene expression; however, the existing methods generally ignore the potential sensitive information that may be exposed in the process. In this work, we utilize the -differential privacy model to provide provable privacy guarantees which is independent of attackers’ background knowledge. Our method makes use of sample databases to prune the generated candidate motifs to lower the magnitude of added noise. Furthermore, to improve the utility of mining results, a strategy of threshold modification is designed to reduce the propagation and random sampling errors in the mining process. Extensive experiments on actual DNA databases confirm that our approach can privately find DNA motifs with high utility and efficiency.

中文翻译：

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基于采样候选剪枝的安全DNA基序发现方法

随着基因研究的不断探索，逐渐暴露的隐私问题成为限制其发展的瓶颈。DNA基序发现是了解基因表达调控的重要研究；然而，现有的方法通常忽略了过程中可能暴露的潜在敏感信息。在这项工作中，我们利用 -差分隐私模型，提供独立于攻击者背景知识的可证明隐私保证。我们的方法利用样本数据库来修剪生成的候选主题，以降低添加噪声的幅度。此外，为了提高挖掘结果的效用，设计了阈值修改策略，以减少挖掘过程中的传播和随机抽样误差。对实际 DNA 数据库的大量实验证实，我们的方法可以私下找到具有高实用性和效率的 DNA 基序。