ANALYSIS OF PRIVACY COMPLIANCE IN BOOTSTRAPPING OF BIGDATA SYSTEMS

Abstract

Bootstrap is a computer approach to get statistical accuracy. It is applied to a wide variety of
statistical procedures like non parametric regressions, classification trees or density estimation.
This technique requires fewer assumptions and offers greater accuracy and insight than other
standard methods for many problems. With the rapid increase in cloud services collecting and
using user data to offer personalized experiences, ensuring that these services comply with their
privacy policies has become a business imperative for building user trust. This paper mainly
focus on two techniques (a) LEGALEASE and (b) GROK that can be use to maintain priavacy
in bootstrapping of big data.