Privacy Preservation in Big Data

Abstract

Big data has brought a revolution in the world of data analytics. Data that was discarded a few years back is now considered a powerful asset. Big data is now being extensively used for knowledge discovery by all sectors of society. It is produced by almost all digital processes and is stored, shared on web. This reliance of big data on web model poses serious security concerns. Traditional security methods cannot be applied to big data due to its large volume, variety and volume. Also since big data contains person specific information, privacy is a major security concern. The three important privacy preservation methods are: data anonymization, notice and consent and differential privacy. In this paper we discuss these privacy preservation methods for big data and how differential privacy is a better solution for big data privacy.

Privacy Preservation in Big Data using K-Anonymity Algorithm with Privacy Key

“…During Data analytics process the less sensitive data can also be encrypted which is useful and may be not accessible to users. Privacy can also be destroyed if the attackers attacks before encryption [2].…”

“…Thus it can be stated that Cryptography cannot ensure the privacy preservation alone, cryptography methods can be used with other methods to ensure a great level of privacy preservation [2].…”

“…Big data is processed and collected so rapidly that is notice is also a burden to individuals to consent each time. An optional notice and consent system can be used for big data to resolve the so rapid notice and consent [2].…”

“…In Notice and Consent method consumer information is shared only after obtaining consent from the user when using a new app or a new web service [2].…”

“…It is a method enabling analysts to extract useful information from databases containing personal information while preserving strong individual privacy protections [2].…”