Differential privacy: its technological prescriptive using big data

被引：38

作者：

Jain P. ^{[1
]}

Gyanchandani M. ^{[1
]}

Khare N. ^{[1
]}

机构：

[1] Computer Science & Engineering, MANIT, Bhopal, MP

来源：

Journal of Big Data | 2018年 / 5卷 / 01期

关键词：

Airavat; Big data; Big data privacy; Differential privacy; Exponential; Geo-indistinguishability; GUPT; Laplace; PINQ; Privacy budget; Sensitivity;

D O I：

10.1186/s40537-018-0124-9

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data is being produced in large amounts and in rapid pace which is diverse in quality, hence, the term big data used. Now, big data has started to influence modern day life in almost every sphere, be it business, education or healthcare. Data being a part and parcel of everyday life, privacy has become a topic requiring emphasis. Privacy can be defined as the capacity of a person or group to seclude themselves or information about themselves, and thereby express them selectively. Privacy in big data can be achieved through various means but here the focus is on differential privacy. Differential privacy is one such field with one of the strongest mathematical guarantee and with a large scope of future development. Along these lines, in this paper, the fundamental ideas of sensitivity and privacy budget in differential privacy, the noise mechanisms utilized as a part of differential privacy, the composition properties, the ways through which it can be achieved and the developments in this field till date has been presented. The research gap and future directions have also been mentioned as part of this paper. © 2018, The Author(s).

引用

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