Compressive analysis and the Future of Privacy

TL;DR

This paper explores how compressive analysis techniques like compression, encoding, encryption, and hashing can be used to enhance individual privacy by enabling customizable privacy-preserving frameworks and policies.

Contribution

It provides an analysis of current technologies and highlights the potential advantages of using compressive analysis for privacy preservation.

Findings

Compressive analysis techniques can facilitate customizable privacy solutions.

Current implementations show potential for improved privacy-preserving frameworks.

The paper discusses the trade-offs between digital service convenience and privacy.

Abstract

Compressive analysis is the name given to the family of techniques that map raw data to their smaller representation. Largely, this includes data compression, data encoding, data encryption, and hashing. In this paper, we analyse the prospects of such technologies in realising customisable individual privacy. We enlist the dire needs to establish privacy preserving frameworks and policies and how can individuals achieve a trade-off between the comfort of an intuitive digital service ensemble and their privacy. We examine the current technologies being implemented, and suggest the crucial advantages of compressive analysis.