Privacy-preserving genomic data publishing via differentially-private suffix tree

Tanya Khatri; Gaby G. Dagher; Yantian Hou

doi:10.1007/978-3-030-37228-6_28

Privacy-preserving genomic data publishing via differentially-private suffix tree

Tanya Khatri
, Gaby G. Dagher
, Yantian Hou

Computer Science Department

Boise State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Privacy-preserving data publishing is a mechanism for sharing data while ensuring that the privacy of individuals is preserved in the published data, and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health researches. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data. In this paper, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes the Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries.

Original language	American English
Title of host publication	Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings
Editors	Songqing Chen, Kim-Kwang Raymond Choo, Xinwen Fu, Wenjing Lou, Aziz Mohaisen
Pages	569-584
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-030-37228-6_28
State	Published - 2019
Event	15th International Conference on Security and Privacy in Communication Networks, SecureComm 2019 - Orlando , United States Duration: 23 Oct 2019 → 25 Oct 2019

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	304 LNICST
ISSN (Print)	1867-8211

Conference

Conference	15th International Conference on Security and Privacy in Communication Networks, SecureComm 2019
Country/Territory	United States
City	Orlando
Period	23/10/19 → 25/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

EGS Disciplines

Computer Sciences

Access to Document

10.1007/978-3-030-37228-6_28

https://doi.org/10.1007/978-3-030-37228-6_28

Cite this

Khatri, T., Dagher, G. G., & Hou, Y. (2019). Privacy-preserving genomic data publishing via differentially-private suffix tree. In S. Chen, K.-K. R. Choo, X. Fu, W. Lou, & A. Mohaisen (Eds.), Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings (pp. 569-584). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 304 LNICST). https://doi.org/10.1007/978-3-030-37228-6_28

Khatri, Tanya ; Dagher, Gaby G. ; Hou, Yantian. / Privacy-preserving genomic data publishing via differentially-private suffix tree. Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings. editor / Songqing Chen ; Kim-Kwang Raymond Choo ; Xinwen Fu ; Wenjing Lou ; Aziz Mohaisen. 2019. pp. 569-584 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{cf1e37ec288e4ba1a68fdf9ed117abb4,

title = "Privacy-preserving genomic data publishing via differentially-private suffix tree",

abstract = "Privacy-preserving data publishing is a mechanism for sharing data while ensuring that the privacy of individuals is preserved in the published data, and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health researches. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data. In this paper, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes the Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries.",

author = "Tanya Khatri and Dagher, \{Gaby G.\} and Yantian Hou",

note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.; 15th International Conference on Security and Privacy in Communication Networks, SecureComm 2019 ; Conference date: 23-10-2019 Through 25-10-2019",

year = "2019",

doi = "10.1007/978-3-030-37228-6\_28",

language = "American English",

isbn = "9783030372279",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

pages = "569--584",

editor = "Songqing Chen and Choo, \{Kim-Kwang Raymond\} and Xinwen Fu and Wenjing Lou and Aziz Mohaisen",

booktitle = "Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings",

}

Khatri, T, Dagher, GG & Hou, Y 2019, Privacy-preserving genomic data publishing via differentially-private suffix tree. in S Chen, K-KR Choo, X Fu, W Lou & A Mohaisen (eds), Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 304 LNICST, pp. 569-584, 15th International Conference on Security and Privacy in Communication Networks, SecureComm 2019, Orlando , United States, 23/10/19. https://doi.org/10.1007/978-3-030-37228-6_28

Privacy-preserving genomic data publishing via differentially-private suffix tree. / Khatri, Tanya; Dagher, Gaby G.; Hou, Yantian.
Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings. ed. / Songqing Chen; Kim-Kwang Raymond Choo; Xinwen Fu; Wenjing Lou; Aziz Mohaisen. 2019. p. 569-584 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 304 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Privacy-preserving genomic data publishing via differentially-private suffix tree

AU - Khatri, Tanya

AU - Dagher, Gaby G.

AU - Hou, Yantian

N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.

PY - 2019

Y1 - 2019

N2 - Privacy-preserving data publishing is a mechanism for sharing data while ensuring that the privacy of individuals is preserved in the published data, and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health researches. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data. In this paper, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes the Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries.

AB - Privacy-preserving data publishing is a mechanism for sharing data while ensuring that the privacy of individuals is preserved in the published data, and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health researches. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data. In this paper, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes the Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries.

UR - https://www.scopus.com/pages/publications/85077503059

U2 - 10.1007/978-3-030-37228-6_28

DO - 10.1007/978-3-030-37228-6_28

M3 - Conference contribution

SN - 9783030372279

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 569

EP - 584

BT - Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings

A2 - Chen, Songqing

A2 - Choo, Kim-Kwang Raymond

A2 - Fu, Xinwen

A2 - Lou, Wenjing

A2 - Mohaisen, Aziz

T2 - 15th International Conference on Security and Privacy in Communication Networks, SecureComm 2019

Y2 - 23 October 2019 through 25 October 2019

ER -

Khatri T, Dagher GG, Hou Y. Privacy-preserving genomic data publishing via differentially-private suffix tree. In Chen S, Choo KKR, Fu X, Lou W, Mohaisen A, editors, Security and Privacy in Communication Networks - 15th EAI International Conference, SecureComm 2019, Proceedings. 2019. p. 569-584. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-37228-6_28

Privacy-preserving genomic data publishing via differentially-private suffix tree

Abstract

Publication series

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UN SDGs

EGS Disciplines

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