Differential Privacy in Quantum Computation

Zhou, L; Ying, M

Differential Privacy in Quantum Computation

Zhou, L Ying, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE Computer Security Foundations Symposium, 2017, pp. 249 - 262
Issue Date:: 2017-09-25

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Field	Value	Language
dc.contributor.author	Zhou, L	en_US
dc.contributor.author	Ying, M https://orcid.org/0000-0003-4847-702X	en_US
dc.date.issued	2017-09-25	en_US
dc.identifier.citation	Proceedings - IEEE Computer Security Foundations Symposium, 2017, pp. 249 - 262	en_US
dc.identifier.isbn	9781538632161	en_US
dc.identifier.issn	1940-1434	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126786
dc.description.abstract	© 2017 IEEE. More and more quantum algorithms have been designed for solving problems in machine learning, database search and data analytics. An important problem then arises: how privacy can be protected when these algorithms are used on private data? For classical computing, the notion of differential privacy provides a very useful conceptual framework in which a great number of mechanisms that protect privacy by introducing certain noises into algorithms have been successfully developed. This paper defines a notion of differential privacy for quantum information processing. We carefully examine how the mechanisms using three important types of quantum noise, the amplitude/phase damping and depolarizing, can protect differential privacy. A composition theorem is proved that enables us to combine multiple privacy-preserving operations in quantum information processing.	en_US
dc.relation.ispartof	Proceedings - IEEE Computer Security Foundations Symposium	en_US
dc.relation.isbasedon	10.1109/CSF.2017.23	en_US
dc.title	Differential Privacy in Quantum Computation	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
pubs.embargo.period	Not known	en_US
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/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. More and more quantum algorithms have been designed for solving problems in machine learning, database search and data analytics. An important problem then arises: how privacy can be protected when these algorithms are used on private data? For classical computing, the notion of differential privacy provides a very useful conceptual framework in which a great number of mechanisms that protect privacy by introducing certain noises into algorithms have been successfully developed. This paper defines a notion of differential privacy for quantum information processing. We carefully examine how the mechanisms using three important types of quantum noise, the amplitude/phase damping and depolarizing, can protect differential privacy. A composition theorem is proved that enables us to combine multiple privacy-preserving operations in quantum information processing.

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http://hdl.handle.net/10453/126786