Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Pedro Sanches, Eric-Oluf Svee, Markus Bylund, Benjamin Hirsch, Magnus Boman

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.

Full Text: PDF DOI: 10.5539/nct.v2n1p34

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Network and Communication Technologies   ISSN 1927-064X(Print)   ISSN 1927-0658 (Online)
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