Privacy-preserving federated discovery of DNA motifs with differential privacy

EXPERT SYSTEMS WITH APPLICATIONS(2024)

Cited 0|Views64
No score
Abstract
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 epsilon-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.
More
Translated text
Key words
DNA,Sequence motif discovery,Sequential pattern,Federated learning,Differential privacy
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined