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Secure Decision Forest Evaluation

Slim Bettaieb 1 Loic Bidoux 1 Olivier Blazy 2 Baptiste Cottier 1, 3, 4 David Pointcheval 3, 4 
3 CASCADE - Construction and Analysis of Systems for Confidentiality and Authenticity of Data and Entities
DI-ENS - Département d'informatique - ENS Paris, CNRS - Centre National de la Recherche Scientifique : UMR 8548, Inria de Paris
Abstract : Decision forests are classical models to efficiently make decision on complex inputs with multiple features. While the global structure of the trees or forests is public, sensitive information have to be protected during the evaluation of some client inputs with respect to some server model. Indeed, the comparison thresholds on the server side may have economical value while the client inputs might be critical personal data. In addition, soundness is also important for the receiver. In our case, we will consider the server to be interested in the outcome of the model evaluation so that the client should not be able to bias it. In this paper, we propose a new offline/online protocol between a client and a server with a constant number of rounds in the online phase, with both privacy and soundness against malicious clients. CCS Concepts: • Security and Privacy → Cryptography.
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Submitted on : Wednesday, August 18, 2021 - 4:03:25 PM
Last modification on : Wednesday, June 8, 2022 - 12:50:03 PM
Long-term archiving on: : Friday, November 19, 2021 - 6:02:32 PM


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Slim Bettaieb, Loic Bidoux, Olivier Blazy, Baptiste Cottier, David Pointcheval. Secure Decision Forest Evaluation. ARES 2021 - 16th International Conference on Availability, Reliability and Security, Aug 2021, Vienna, Austria. pp.1-12, ⟨10.1145/3465481.3465763⟩. ⟨hal-03321368⟩



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