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Private Client-Side Profiling with Random Forests and Hidden Markov Models

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Privacy Enhancing Technologies (PETS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 7384))

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Abstract

Nowadays, service providers gather fine-grained data about users to deliver personalized services, for example, through the use of third-party cookies or social network profiles. This poses a threat both to privacy, since the amount of information obtained is excessive for the purpose of customization, and authenticity, because those methods employed to gather data can be blocked and fooled.

In this paper we propose privacy-preserving profiling techniques, in which users perform the profiling task locally, reveal to service providers the result and prove its correctness. We address how our approach applies to tasks of both classification and pattern recognition. For the former, we describe client-side profiling based on random forests, where users, based on certified input data representing their activity, resolve a random forest and reveal the classification result to service providers. For the latter, we show how to match a stream of user activity to a regular expression, or how to assign it a probability using a hidden Markov model. Our techniques, based on the use of zero-knowledge proofs, can be composed with other protocols as part of the certification of a larger computation.

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Author information

Authors and Affiliations

  1. Microsoft Research, USA

    George Danezis, Markulf Kohlweiss & Benjamin Livshits

  2. IBBT and KU Leuven, ESAT-COSIC, Belgium

    Alfredo Rial

Authors
  1. George Danezis
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  2. Markulf Kohlweiss
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  3. Benjamin Livshits
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  4. Alfredo Rial
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Editors and Affiliations

  1. Karlstad University, Universitetsgatan 2, 65188, Karlstad, Sweden

    Simone Fischer-Hübner

  2. Department of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Blvd., 76019, Arlington, TX, USA

    Matthew Wright

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Danezis, G., Kohlweiss, M., Livshits, B., Rial, A. (2012). Private Client-Side Profiling with Random Forests and Hidden Markov Models. In: Fischer-Hübner, S., Wright, M. (eds) Privacy Enhancing Technologies. PETS 2012. Lecture Notes in Computer Science, vol 7384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31680-7_2

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  • DOI: https://doi.org/10.1007/978-3-642-31680-7_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31679-1

  • Online ISBN: 978-3-642-31680-7

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