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Privately Querying Privacy: Privacy Estimation with Guaranteed Privacy of User and Database Party

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Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13084))

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Abstract

Many fields of science and industry increasingly rely on the availability of individualized data that a given user may or may not be willing to share. Anonymization methods are often employed to improve user acceptance or are even required by law. However, the user has to rely on the technical competence and faithfulness of the database provider in implementing such measures, and even for honest and competent providers, sophisticated attacks on anonymity may still work. Consequently, many measures have been developed to allow users to estimate their anonymity in a given dataset; however, these measures are typically applied after a user has already offered his data to the provider. Here, we demonstrate how protocols based on cryptographic techniques such as secure multiparty computation and oblivious transfer can be employed to allow a user to query anonymization measures before revealing his own data without compromising the database privacy along the way.

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Notes

  1. 1.

    Please note that the multi-index \(\mathcal {I}_1\mathcal {I}_2\dots \mathcal {I}_n\) has to be converted into a scalar index for standard OT implementations. This can be achieved by, e.g., shifting each \(\mathcal {I}_j\) by \(\sum _{k=1}^{j-1}\lceil \log _2(\#h_k)\rceil \) bits before combining all into a single number.

  2. 2.

    Notice that it might, at first glance, seem reasonable to use a simpler and more efficient XOR instead of \(\lnot X^i_j \wedge X^i_{j\oplus _{\kappa }1}\); unfortunately, this does not work because it results in a value of 1 at the boundary of the last to the first interval and hence would reveal \(\rho _i\).

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Funding

This work was supported by the German Ministry of Education and Research through the DaSoMan-project [16KIS0804 to AKH].

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Authors and Affiliations

  1. MONDATA GmbH, Saarbrücken, Germany

    Anna Katharina Hildebrandt

  2. Johannes-Gutenberg University Mainz, Mainz, Germany

    Ernst Althaus & Andreas Hildebrandt

Authors
  1. Anna Katharina Hildebrandt
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  2. Ernst Althaus
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  3. Andreas Hildebrandt
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Corresponding authors

Correspondence to Ernst Althaus or Andreas Hildebrandt .

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Editors and Affiliations

  1. Gran Sasso Science Institute (GSSI), L’Aquila, Italy

    Gianlorenzo D’Angelo

  2. University of Liverpool, Liverpool, UK

    Othon Michail

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Hildebrandt, A.K., Althaus, E., Hildebrandt, A. (2021). Privately Querying Privacy: Privacy Estimation with Guaranteed Privacy of User and Database Party. In: D’Angelo, G., Michail, O. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2021. Lecture Notes in Computer Science(), vol 13084. Springer, Cham. https://doi.org/10.1007/978-3-030-93043-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-93043-1_4

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