Machine Learning with Membership Privacy using Adversarial Regularization

10.1145/3243734.3243855

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Bibliographic Details
Main Authors:	Nasr, Milad, Shokri, Reza, Houmansadr, Amir
Other Authors:	DEPT OF COMPUTER SCIENCE
Format:	Conference or Workshop Item
Published:	ASSOC COMPUTING MACHINERY 2020
Subjects:	Science & Technology Technology Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Data privacy Machine learning Inference attacks Membership privacy Indistinguishability Min-max game Adversarial process
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/172810
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-172810
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spelling	sg-nus-scholar.10635-1728102023-09-13T21:45:19Z Machine Learning with Membership Privacy using Adversarial Regularization Nasr, Milad Shokri, Reza Houmansadr, Amir DEPT OF COMPUTER SCIENCE Science & Technology Technology Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Data privacy Machine learning Inference attacks Membership privacy Indistinguishability Min-max game Adversarial process 10.1145/3243734.3243855 ACM SIGSAC Conference on Computer and Communications Security (CCS) abs/1807.05852 634-646 completed 2020-08-17T05:39:08Z 2020-08-17T05:39:08Z 2018-01-01 2020-08-16T16:52:48Z Conference Paper Nasr, Milad, Shokri, Reza, Houmansadr, Amir (2018-01-01). Machine Learning with Membership Privacy using Adversarial Regularization. ACM SIGSAC Conference on Computer and Communications Security (CCS) abs/1807.05852 : 634-646. ScholarBank@NUS Repository. https://doi.org/10.1145/3243734.3243855 9781450356930 https://scholarbank.nus.edu.sg/handle/10635/172810 ASSOC COMPUTING MACHINERY Elements
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Science & Technology Technology Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Data privacy Machine learning Inference attacks Membership privacy Indistinguishability Min-max game Adversarial process
spellingShingle	Science & Technology Technology Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Data privacy Machine learning Inference attacks Membership privacy Indistinguishability Min-max game Adversarial process Nasr, Milad Shokri, Reza Houmansadr, Amir Machine Learning with Membership Privacy using Adversarial Regularization
description	10.1145/3243734.3243855
author2	DEPT OF COMPUTER SCIENCE
author_facet	DEPT OF COMPUTER SCIENCE Nasr, Milad Shokri, Reza Houmansadr, Amir
format	Conference or Workshop Item
author	Nasr, Milad Shokri, Reza Houmansadr, Amir
author_sort	Nasr, Milad
title	Machine Learning with Membership Privacy using Adversarial Regularization
title_short	Machine Learning with Membership Privacy using Adversarial Regularization
title_full	Machine Learning with Membership Privacy using Adversarial Regularization
title_fullStr	Machine Learning with Membership Privacy using Adversarial Regularization
title_full_unstemmed	Machine Learning with Membership Privacy using Adversarial Regularization
title_sort	machine learning with membership privacy using adversarial regularization
publisher	ASSOC COMPUTING MACHINERY
publishDate	2020
url	https://scholarbank.nus.edu.sg/handle/10635/172810
_version_	1778169601674182656

Machine Learning with Membership Privacy using Adversarial Regularization

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