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Generative Adversarial Nets Enhanced Continual Data Release Using Differential Privacy
conference contribution
posted on 2020-01-01, 00:00 authored by Stella HoStella Ho, Youyang Qu, Longxiang GaoLongxiang Gao, Jianxin LiJianxin Li, Yong XiangYong Xiang© 2020, Springer Nature Switzerland AG. In the era of big data, increasing massive volume of data is generated and published consecutively for both research and commercial purposes. The potential value of sensitive information also attracts interest from adversaries and thereby arises public concern. Current research mostly focuses on privacy-preserving data release in a statistic manner rather than taking the dynamics and correlation of context into consideration. Motivated by this, a novel idea is proposed by combining differential privacy and generative adversarial nets. Generative adversarial nets and its extensions are used to generate a synthetic data set with indistinguishable statistic features while differential privacy guarantees a trade-off between the privacy protection and data utility. Extensive simulation results on real-world data set testify the superiority of the proposed model in terms of privacy protection and improved data utility.
History
Event
Algorithms and Architectures for Parallel Processing. Conference (2019 : 19th : Melbourne, Victoria)Series
Lecture Notes in Computer Science; 11945Pagination
418 - 426Publisher
SpringerLocation
Melbourne, VictoriaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2019-12-09End date
2019-12-11ISSN
0302-9743eISSN
1611-3349ISBN-13
9783030389604Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
S Wen, A Zomaya, L YangTitle of proceedings
ICA3PP 2019 : Algorithms and architectures for parallel processing : 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9-11, 2019, ProceedingsUsage metrics
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