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Context Hiding Multi-key Linearly Homomorphic Authenticators

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Topics in Cryptology – CT-RSA 2019 (CT-RSA 2019)

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Abstract

Demanding computations are increasingly outsourced to cloud platforms. For such outsourced computations, the efficient verifiability of results is a crucial requirement. When sensitive data is involved, the verification of a computation should preserve the privacy of the input values: it should be context hiding. Context hiding verifiability is enabled by existing homomorphic authenticator schemes. However, until now, no context hiding homomorphic authenticator scheme supports multiple independent clients, e.g. multiple keys. Multi-key support is necessary for datasets involving input authenticated by different clients, e.g. multiple hospitals in e-health scenarios. In this paper, we propose the first perfectly context hiding, publicly verifiable multi-key homomorphic authenticator scheme supporting linear functions. Our scheme is provably unforgeable in the standard model, and succinct. Verification time depends only linearly on the number of clients, in an amortized sense.

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Acknowledgments

This work has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 644962.

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Authors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Lucas Schabhüser, Denis Butin & Johannes Buchmann

Authors
  1. Lucas Schabhüser
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  2. Denis Butin
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  3. Johannes Buchmann
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Corresponding author

Correspondence to Lucas Schabhüser .

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Editors and Affiliations

  1. Mitsubishi Electric Corporation, Kamakura, Japan

    Mitsuru Matsui

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Schabhüser, L., Butin, D., Buchmann, J. (2019). Context Hiding Multi-key Linearly Homomorphic Authenticators. In: Matsui, M. (eds) Topics in Cryptology – CT-RSA 2019. CT-RSA 2019. Lecture Notes in Computer Science(), vol 11405. Springer, Cham. https://doi.org/10.1007/978-3-030-12612-4_25

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  • DOI: https://doi.org/10.1007/978-3-030-12612-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12611-7

  • Online ISBN: 978-3-030-12612-4

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