ANALYSIS OF PRIVACY COMPLIANCE OF BOOTSTRAPPING IN BIG DATA 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