research-article

Secure DNA Motif-Finding Method Based on Sampling Candidate Pruning

Authors:

Huanhuan WangAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 3

Article No.: 58, Pages 1 - 19

https://doi.org/10.1145/3382078

Published: 19 August 2021 Publication History

Abstract

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.

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Cited By

Li JFeng MLi S(2024)A Deep Learning Cache Framework for Privacy Security on Heterogeneous IoT NetworksIEEE Access10.1109/ACCESS.2024.342248712(93261-93269)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3422487
Chen YGan WHuang GWu YYu P(2024)Privacy-preserving federated discovery of DNA motifs with differential privacyExpert Systems with Applications10.1016/j.eswa.2024.123799249(123799)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123799
Wang HZhang XXia YWu X(2023)An intelligent blockchain-based access control framework with federated learning for genome-wide association studiesComputer Standards & Interfaces10.1016/j.csi.2022.10369484:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.csi.2022.103694
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Index Terms

Secure DNA Motif-Finding Method Based on Sampling Candidate Pruning

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 3

August 2021

522 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3468071

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2021

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 December 2019

Published in TOIT Volume 21, Issue 3

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Cited By

Li JFeng MLi S(2024)A Deep Learning Cache Framework for Privacy Security on Heterogeneous IoT NetworksIEEE Access10.1109/ACCESS.2024.342248712(93261-93269)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3422487
Chen YGan WHuang GWu YYu P(2024)Privacy-preserving federated discovery of DNA motifs with differential privacyExpert Systems with Applications10.1016/j.eswa.2024.123799249(123799)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123799
Wang HZhang XXia YWu X(2023)An intelligent blockchain-based access control framework with federated learning for genome-wide association studiesComputer Standards & Interfaces10.1016/j.csi.2022.10369484:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.csi.2022.103694
Lou Y(2022)Construction of the Human-Computer Interaction Model of Hybrid Course Based on Machine LearningMathematical Problems in Engineering10.1155/2022/58367102022(1-10)Online publication date: 27-Apr-2022
https://doi.org/10.1155/2022/5836710
Wang C(2022)A Dynamic CGE Model for Consumer Trust Mechanism within an E-Commerce MarketMobile Information Systems10.1155/2022/52206542022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5220654

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