A Decentralized Private Data Transaction Pricing and Quality Control Method

Yao, L; Jia, Y; Zhang, H; Long, K; Pan, M; Yu, S

A Decentralized Private Data Transaction Pricing and Quality Control Method

Yao, L Jia, Y Zhang, H Long, K Pan, M Yu, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Communications, 2019, 2019-May
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Yao, L
dc.contributor.author	Jia, Y
dc.contributor.author	Zhang, H
dc.contributor.author	Long, K
dc.contributor.author	Pan, M
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date	2019-05-20
dc.date.accessioned	2020-06-12T06:39:42Z
dc.date.available	2020-06-12T06:39:42Z
dc.date.issued	2019-05-01
dc.identifier.citation	IEEE International Conference on Communications, 2019, 2019-May
dc.identifier.isbn	9781538680889
dc.identifier.issn	1550-3607
dc.identifier.uri	http://hdl.handle.net/10453/141383
dc.description.abstract	© 2019 IEEE. In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Communications
dc.relation.ispartof	IEEE International Conference on Communications
dc.relation.isbasedon	10.1109/ICC.2019.8761577
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	A Decentralized Private Data Transaction Pricing and Quality Control Method
dc.type	Conference Proceeding
utslib.citation.volume	2019-May
utslib.location.activity	Shanghai, China
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-12T06:39:38Z
pubs.finish-date	2019-05-24
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-05-20
pubs.volume	2019-May
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.

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http://hdl.handle.net/10453/141383