title: A private DNA motif finding algorithm.
abstract: With the increasing availability of genomic sequence data, numerous methods have been proposed for finding DNA motifs. The discovery of DNA motifs serves a critical step in many biological applications. However, the privacy implication of DNA analysis is normally neglected in the existing methods. In this work, we propose a private DNA motif finding algorithm in which a DNA owner's privacy is protected by a rigorous privacy model, known as ∊-differential privacy. It provides provable privacy guarantees that are independent of adversaries' background knowledge. Our algorithm makes use of the n-gram model and is optimized for processing large-scale DNA sequences. We evaluate the performance of our algorithm over real-life genomic data and demonstrate the promise of integrating privacy into DNA motif finding.
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong. Electronic address: ruichen@comp.hkbu.edu.hk.
authors.forename: Rui
authors.initials: R
authors.lastname: Chen
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore. Electronic address: pengyun@ntu.edu.sg.
authors.forename: Yun
authors.initials: Y
authors.lastname: Peng
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong. Electronic address: bchoi@comp.hkbu.edu.hk.
authors.forename: Byron
authors.initials: B
authors.lastname: Choi
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong. Electronic address: xujl@comp.hkbu.edu.hk.
authors.forename: Jianliang
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong. Electronic address: haibo@comp.hkbu.edu.hk.
authors.forename: Haibo
authors.initials: H
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D004247:DNA / Q000235:genetics*; D018907:Privacy
chemical_list: D004247:DNA
keywords: Motif finding; Privacy protection; n-gram model; ∊-Differential privacy