The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for Urban Big Data Analytics

TL;DR

This paper proposes the principle of least sensing, a new paradigm for privacy-friendly urban big data analytics that complies with data protection laws by minimizing sensing requirements.

Contribution

It introduces the principle of least sensing as a novel approach to enable law-regulated big data analytics in urban environments.

Findings

The principle reduces sensing data collection to enhance privacy.

It provides a framework for law-compliant urban data analytics.

Potential for improved privacy preservation in big data applications.

Abstract

With the worldwide emergence of data protection regulations, how to conduct law-regulated big data analytics becomes a challenging and fundamental problem. This article introduces the principle of least sensing, a promising sensing paradigm toward law-regulated big data analytics.