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Knowing Your Population: Privacy-Sensitive Mining of Massive Data
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0001-5620-6305
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2013 (English)In: Network and Communication Technologies, ISSN 1927-064X, Vol. 2, no 1, 34-51 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Canadian Center of Science and Education , 2013. Vol. 2, no 1, 34-51 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-139379DOI: 10.5539/nct.v2n1p34OAI: diva2:686141

QC 20140114

Available from: 2014-01-10 Created: 2014-01-10 Last updated: 2015-01-14Bibliographically approved
In thesis
1. Health Data: Representation and (In)visibility
Open this publication in new window or tab >>Health Data: Representation and (In)visibility
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Health data requires context to be understood. I show how, by examining two areas: self-surveillance, with a focus on representation of bodily data, and mass-surveillance, with a focus on representing populations. I critically explore how Information and Communication Technology (ICT) can be made to represent individuals and populations, and identify implications of such representations. My contributions are: (i) the design of a self-tracking stress management system, (ii) the design of a mass-surveillance system based on mobile phone data, (iii) an empirical study exploring how users of a fitness tracker make sense of their generated data, (iv) an analysis of the discourse of designers of a syndrome surveillance system, (v) a critical analysis of the design process of a mass-surveillance system, and (vi) an analysis of the historicity of the concepts and decisions taken during the design of a stress management system. I show that producing health data, and subsequently the technological characteristics of algorithms that produce them depend on factors present in the ICT design process. These factors determine how data is made to represent individuals and populations in ways that may selectively make invisible parts of the population, determinants of health, or individual conception of self and wellbeing. In addition, I show that the work of producing data does not stop with the work of the engineers who produce ICT-based systems: maintenance is constantly required.

Abstract [sv]

För att förstå hälsodata krävs sammanhang. Jag visar hur detta kan erhållas, genom två fallstudier: en om självövervakning, med fokus på representation av kroppsdata, samt en om massövervakning, med fokus på representation av populationer. Jag granskar kritiskt hur informationsteknologi (IT) kan fås att representera såväl individer som populationer och vilka följder det får. Mina bidrag är: (i) utformningen av ett självövervakningssystem för stresshantering, (ii) utformningen av ett massövervakningssystem baserat på data från mobiltelefonanvändning, (iii) en empirisk studie av hur användare av en hälsosensor begriper det data som sensorn genererar, (iv) en diskursiv analys av hur syndromövervakningssystem utformas, (v) en kritisk analys av processer kring att utforma ett massövervakningssystem, samt (vi) en analys av den historiska korrektheten i begrepp och beslutsfattande i samband med utformningen av ett stresshanteringssystem. Jag visar att produktion av hälsodata, liksom tekniska beskrivningar av de algoritmer som används i den processen, beror av faktorer som hänger samman med IT-utformningsprocessen. Dessa faktorer avgör sedan hur data kan fås att representera individer och populationer på sätt som kan rendera delar av en population, hälsodeterminanter, eller individens självuppfattning och förståelse av välmående osynliga. Jag visar också att arbetet med att producera data inte är avslutat i och med det ingenjörsarbete som krävs för att IT-systemen ska byggas: konstant underhåll krävs också.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. 61 p.
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 14:17
SICS Dissertation Series, ISSN 1101-1335 ; 72
National Category
Computer Science
urn:nbn:se:kth:diva-158909 (URN)978-91-7595-403-5 (ISBN)
Public defence
2015-01-29, Sal B, Electrum, KTH-ICT, Isafordsgatan 16, Kista, 14:00 (English)

QC 20150114

Available from: 2015-01-14 Created: 2015-01-13 Last updated: 2015-03-03Bibliographically approved

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