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Privacy-Preserving Data Integration Using Decoupled Data

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Book cover Security and Privacy in Social Networks

Abstract

Data from social network websites are an excellent source of information for studying human behavior and interactions. Typically, when analyzing such data, the default mode of access is de-identified data, which provides a level of privacy protection. However, due to its inability to link to other data, de-identified data has limitations with regard to answering broad and critically important questions about our complex society. In this study, we investigate the properties of information related to privacy, and we present a novel model of data access called decoupled data access for studying personal data using these properties. “Decoupling” refers to separating out the identifying information from the sensitive data that needs protection. We suggest that decoupled data access can provide flexible data integration with error management while providing the same level of privacy protection as de-identified data. We further test the ability of different mechanisms to hinder inference of identity when names are revealed for data integration. Our results show that through chaffing, not specifying the universe around the data, and revealing names in isolation, the real identities of names for both common and rare names can be protected.

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Acknowledgement

We thank everyone who participated in the survey. We also thank Mike Reiter and Fred Brooks for their insightful comments, and Gautam Sanka, Ian Sang-Jun Kim, and Ren Bauer for their assistance with the experiment. This research was supported in part by funding from the NC Department of Health and Human Services and by NSF award no. CNS-0915364. The authors gratefully acknowledge their support.

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Authors and Affiliations

  1. Department of Computer Science, School of Social Work, University of North Carolina (UNC-CH), Chapel Hill, NC, USA

    Hye-Chung Kum

  2. Renaissance Computing Institute (RENCI), Department of Computer Science, University of North Carolina (UNC-CH), Chapel Hill, NC, USA

    Stanley Ahalt

  3. Department of Computer Science, University of North Carolina (UNC-CH), Chapel Hill, NC, USA

    Darshana Pathak

Authors
  1. Hye-Chung Kum
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  2. Stanley Ahalt
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  3. Darshana Pathak
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Corresponding author

Correspondence to Hye-Chung Kum .

Editor information

Editors and Affiliations

  1. , Human Dynamic Group, MIT Media Lab, 77 Mass. Ave, Cambridge, 02139, Massachusetts, USA

    Yaniv Altshuler

  2. , Information Systems Engineering, Ben-Gurion University, n/a, Beer-Sheva, n/a, Israel

    Yuval Elovici

  3. Inst. f. Informatik III, Univ. Bonn, Römerstr. 164, Bonn, 53117, Germany

    Armin B. Cremers

  4. , Human Dynamics Group, MIT Media Lab, 77 Mass. Ave, Cambridge, 02139, Massachusetts, USA

    Nadav Aharony

  5. , Human Dynamics Group, MIT Media Lab, 77 Mass. Ave, Cambridge, 02139, Massachusetts, USA

    Alex Pentland

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Kum, HC., Ahalt, S., Pathak, D. (2013). Privacy-Preserving Data Integration Using Decoupled Data. In: Altshuler, Y., Elovici, Y., Cremers, A., Aharony, N., Pentland, A. (eds) Security and Privacy in Social Networks. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4139-7_11

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  • DOI: https://doi.org/10.1007/978-1-4614-4139-7_11

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  • Print ISBN: 978-1-4614-4138-0

  • Online ISBN: 978-1-4614-4139-7

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