Improving Laplace Mechanism of Differential Privacy by Personalized Sampling

Huang, W; Zhou, S; Zhu, T; Liao, Y; Wu, C; Qiu, S

Improving Laplace Mechanism of Differential Privacy by Personalized Sampling

Huang, W Zhou, S Zhu, T

Liao, Y Wu, C Qiu, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2021, 00, pp. 623-630
Issue Date:: 2021-02-09

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Field	Value	Language
dc.contributor.author	Huang, W
dc.contributor.author	Zhou, S
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Liao, Y
dc.contributor.author	Wu, C
dc.contributor.author	Qiu, S
dc.date	2020-12-29
dc.date.accessioned	2022-01-04T03:33:50Z
dc.date.available	2022-01-04T03:33:50Z
dc.date.issued	2021-02-09
dc.identifier.citation	2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2021, 00, pp. 623-630
dc.identifier.isbn	9781665403924
dc.identifier.uri	http://hdl.handle.net/10453/152651
dc.description.abstract	The differential privacy is the state-of-the-art conception for privacy preservation due to its strong privacy guarantees, however it suffers from low accuracy. In this paper, we propose a personalized sample Laplace mechanism by combining the Laplace mechanism with sampling technology. In order to improve the accuracy, the proposed mechanism assigns personalized sampling probability to each record. Based on the personalized sampling probability, we prove that the proposed mechanism satisfies ε differential privacy. Then we compare the proposed mechanism with other mechanisms in term of the accuracy. Through extensive experiments on synthetic data set and real world data set, we demonstrate that the performance of proposed mechanism is better.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
dc.relation.ispartof	2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications
dc.relation.isbasedon	10.1109/trustcom50675.2020.00088
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Improving Laplace Mechanism of Differential Privacy by Personalized Sampling
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Guangzhou, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-01-04T03:33:49Z
pubs.finish-date	2021-01-01
pubs.publication-status	Published
pubs.start-date	2020-12-29
pubs.volume	00

Abstract:

The differential privacy is the state-of-the-art conception for privacy preservation due to its strong privacy guarantees, however it suffers from low accuracy. In this paper, we propose a personalized sample Laplace mechanism by combining the Laplace mechanism with sampling technology. In order to improve the accuracy, the proposed mechanism assigns personalized sampling probability to each record. Based on the personalized sampling probability, we prove that the proposed mechanism satisfies ε differential privacy. Then we compare the proposed mechanism with other mechanisms in term of the accuracy. Through extensive experiments on synthetic data set and real world data set, we demonstrate that the performance of proposed mechanism is better.

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http://hdl.handle.net/10453/152651