Knowing Your Population: Privacy-Sensitive Mining of Massive Data

TL;DR

This paper presents privacy-sensitive methods for mining telecommunication data to extract mobility patterns useful for transportation and societal applications, balancing data utility with privacy protection.

Contribution

It introduces algorithms that extract location and route patterns from telecommunication data while ensuring privacy, challenging the notion that individual monitoring is necessary for valuable insights.

Findings

Methods comply with privacy laws and best practices.

Patterns of mobility can be derived without compromising individual privacy.

Case study demonstrates practical utility in transportation planning.

Abstract

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practices