Ma, Jixin, Yao, Xiaoming and Zhou, Xiaoyi (2016) Differential privacy of Big Data: An overview. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance. IEEE Computer Society, pp. 7-12. ISBN 978-1-5090-2403-2/16 (doi:10.1109/BigDataSecurity-HPSC-IDS.2016.9)

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Abstract

Differential privacy has seen dramatic development in recent decades as data mining of the statistical private datasets in a distributed big data environment has become an effective paradigm that, it is argued, guarantees the mathematically rigorous privacy of the participants by ensuring the equivalence of the analyzing results with the removal or addition of a single database item. However, challenges relating to the trade-off between privacy and utility still apply with the application of differential privacy. In this survey, we review and re-examine those new improvements of the differential privacy mainly in correlated scenarios, along with different methods of choosing the epsilon for achieving a better trade-off between the privacy and utility of the datasets in conventional settings, so as to build up deeper insights on specific technical aspects of this paradigm and its future trends of development.

Item Type:	Conference Proceedings
Title of Proceedings:	2016 IEEE 2nd International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance
Additional Information:	The 2nd IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2016), New York City, US, April 9th-10th 2016.
Uncontrolled Keywords:	differential privacy, statistical databases, data mining, utility, big data
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Organisation Publisher
Last Modified:	26 Nov 2020 22:34
URI:	http://gala.gre.ac.uk/id/eprint/15452

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