Differential privacy: its technological prescriptive using big data

Priyank Jain,Nilay Khare,Manasi Gyanchandani

doi:10.1186/s40537-018-0124-9

Priyank Jain, Nilay Khare + Show 1 more

Open Access

https://doi.org/10.1186/s40537-018-0124-9

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Abstract

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.

Highlights

Differential privacy [1] is a technology that provides researchers and database analysts a facility to obtain the useful information from the databases that contain personal information of people without revealing the personal identities of the individuals
Latanya Sweeney using the publicly available voter database and database released by Group Insurance Commission (GIC), successfully identified the health record by just comparing and co-relating them
This paper presents the basics of differential privacy as a privacy preserving mechanism [3, 4] for big data

Summary

Introduction

Differential privacy [1] is a technology that provides researchers and database analysts a facility to obtain the useful information from the databases that contain personal information of people without revealing the personal identities of the individuals. This is done by introducing a minimum distraction in the information provided by the database system. The distraction introduced is large enough so that they protect the privacy and at the same time small enough so that the information provided to analyst is still useful. Hiding some information cannot assures the protection of individual identity.

Methods

Conclusion