DNA sequence motif discovery is an important issue in gene research, which helps identify transcription factor binding sites in DNA sequences to reveal the mechanisms that regulate gene expression. However, the growing awareness of privacy protection and increasingly stringent regulations pose challenges to data collection and usage. This paper proposes DP-FLMD, a privacy-preserving federated framework for discovering DNA sequence motifs. We employ federated learning, allowing participants to store their raw data locally and upload only selected parameters to protect data privacy. Nevertheless, privacy concerns still persist, as these participant parameters may potentially compromise their privacy to some extent. To enhance privacy protection, we incorporate a differential privacy algorithm to add noise to these parameters. Additionally, we verify that the method satisfies -differential privacy through theoretical analysis. Extensive experiments are performed on six datasets, demonstrating that DP-FLMD achieves a good trade-off between privacy and utility. Besides, we investigate the effect of parameters on DP-FLMD. In the future, our research will focus on exploring enhanced privacy protection mechanisms, expanding the scope of framework applications, and further optimizing the trade-off between privacy and utility.

中文翻译：

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具有差异隐私的 DNA 基序的隐私保护联合发现

DNA序列基序发现是基因研究中的一个重要问题，它有助于识别DNA序列中的转录因子结合位点，从而揭示调节基因表达的机制。然而，隐私保护意识的增强和监管的日益严格给数据收集和使用带来了挑战。本文提出了 DP-FLMD，一种用于发现 DNA 序列基序的隐私保护联合框架。我们采用联邦学习，允许参与者在本地存储原始数据并仅上传选定的参数以保护数据隐私。尽管如此，隐私问题仍然存在，因为这些参与者参数可能会在某种程度上损害他们的隐私。为了增强隐私保护，我们采用了差分隐私算法来向这些参数添加噪声。此外，我们通过理论分析验证了该方法满足差分隐私。在六个数据集上进行了大量的实验，证明 DP-FLMD 在隐私和实用性之间实现了良好的权衡。此外，我们还研究了参数对 DP-FLMD 的影响。未来我们的研究将重点探索增强的隐私保护机制，扩大框架应用范围，进一步优化隐私与效用之间的权衡。