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Carlos Aguilar

IE2 Temporaire sur CDD  -  UNS

ancien membre

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titre
Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability
auteur
Elif Köksal Ersöz, Carlos Aguilar Melchor, Pascal Chossat, Martin Krupa, Frédéric Lavigne
article
PLoS ONE, 2020, 15 (4), pp.1-28. ⟨10.1371/journal.pone.0231165⟩
annee_publi
2020
resume
In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corresponding to two different types of cerebral functions activation of regular or irregular sequences. The switch between the two types of activation occurs through the modulation of biological parameters, without altering the connectivity matrix. Some of the parameters included in our model are neuronal gain, strength of inhibition, synaptic depression and noise. We investigate how these parameters enable the existence of sequences and influence the type of sequences observed. In particular we show that synaptic depression and noise drive the transitions from one memory item to the next and neuronal gain controls the switching between regular and irregular (random) activation.
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titre
A Comparison of the Homomorphic Encryption Libraries HElib, SEAL and FV-NFLlib
auteur
Carlos Aguilar Melchor, Marc-Olivier Killijian, Cédric Lefebvre, Thomas Ricosset
article
International Conference on Security for Information Technology and Communications (SECITC 2018), Nov 2018, Bucharest, Romania. pp.425-442, ⟨10.1007/978-3-030-12942-2_32⟩
annee_publi
2018
resume
Fully homomorphic encryption has considerably evolved during the past 10 years. In particular, the discovery of more efficient schemes has brought the computational complexity down to acceptable levels for some applications. Several implementations of these schemes have been publicly released, enabling researchers and practitioners to better understand the performance properties of the schemes. This improved understanding of the performance has led to the discovery of new potential applications of homomorphic encryption, fuelling further research on all fronts.In this work, we provide a comparative benchmark of the leading homomorphic encryption libraries HElib, FV-NFLlib, and SEAL for large plaintext moduli of up to 2048 bits, and analyze their relative performance.
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titre
Delegating Elliptic-Curve Operations with Homomorphic Encryption
auteur
Carlos Aguilar Melchor, Jean-Christophe Deneuville, Philippe Gaborit, Tancrède Lepoint, Thomas Ricosset
article
IEEE Conference on Communications and Network Security, May 2018, Pékin, China
annee_publi
2018
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titre
Efficient Encryption From Random Quasi-Cyclic Codes
auteur
Carlos Aguilar Melchor, Olivier Blazy, Jean-Christophe Deneuville, Philippe Gaborit, Gilles Zémor
article
IEEE Transactions on Information Theory, 2018, 64 (5), pp.3927-3943
annee_publi
2018
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titre
BIKE: Bit Flipping Key Encapsulation
auteur
Nicolas Aragon, Paulo S. L. M. Barreto, Slim Bettaieb, Loïc Bidoux, Olivier Blazy, Jean-Christophe Deneuville, Philippe Gaborit, Shay Gueron, Tim Guneysu, Carlos Aguilar Melchor, Rafael Misoczki, Edoardo Persichetti, Nicolas Sendrier, Jean-Pierre Tillich, Gilles Zémor
article
2017
annee_publi
2017
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Autre publication scientifique
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titre
Hamming Quasi-Cyclic (HQC)
auteur
Carlos Aguilar Melchor, Nicolas Aragon, Slim Bettaieb, Loïc Bidoux, Olivier Blazy, Jean-Christophe Deneuville, Philippe Gaborit, Edoardo Persichetti, Gilles Zémor
article
2017, https://pqc-hqc.org/
annee_publi
2017
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titre
Rank Quasi-Cyclic (RQC)
auteur
Carlos Aguilar Melchor, Nicolas Aragon, Slim Bettaieb, Loïc Bidoux, Olivier Blazy, Jean-Christophe Deneuville, Philippe Gaborit, Gilles Zémor
article
2017, https://pqc-rqc.org/
annee_publi
2017
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titre
Ouroboros-R
auteur
Carlos Aguilar Melchor, Nicolas Aragon, Slim Bettaieb, Loïc Bidoux, Olivier Blazy, Jean-Christophe Deneuville, Philippe Gaborit, Adrien Hauteville, Gilles Zémor
article
2017, https://pqc-ouroborosr.org/
annee_publi
2017
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titre
Sampling from Arbitrary Centered Discrete Gaussians for Lattice-Based Cryptography
auteur
Carlos Aguilar Melchor, Martin R. Albrecht, Thomas Ricosset
article
International Conference on Applied Cryptography and Network Security (ACNS 2017), Jul 2017, Kanazawa, Japan. pp.3-19
annee_publi
2017
resume
Non-Centered Discrete Gaussian sampling is a fundamental building block in many lattice-based constructions in cryptography, such as signature and identity-based encryption schemes. On the one hand, the center-dependent approaches, e.g. cumulative distribution tables (CDT), Knuth-Yao, the alias method, discrete Zigurat and their variants, are the fastest known algorithms to sample from a discrete Gaussian distribution. However, they use a relatively large precomputed table for each possible real center in [0,1) making them impracticable for non-centered discrete Gaussian sampling. On the other hand, rejection sampling allows to sample from a discrete Gaussian distribution for all real centers without prohibitive precomputation cost but needs costly floating-point arithmetic and several trials per sample. In this work, we study how to reduce the number of centers for which we have to precompute tables and propose a non-centered CDT algorithm with practicable size of precomputed tables as fast as its centered variant. Finally, we provide some experimental results for our open-source C++ implementation indicating that our sampler increases the rate of Peikert’s algorithm for sampling from arbitrary lattices (and cosets) by a factor 3 with precomputation storage up to 6.2 MB.
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https://hal.science/hal-02548105/file/Aguilar_Melchor_22271.pdf BibTex
titre
Efficient and secure outsourcing of genomic data storage
auteur
João Sá Sousa, Cédric Lefebvre, Zhicong Huang, Jean Louis Raisaro, Carlos Aguilar Melchor, Marc-Olivier Killijian, Jean-Pierre Hubaux
article
BMC Medical Genomics, 2017, 10 (S2), pp.1--17. ⟨10.1186/s12920-017-0275-0⟩
annee_publi
2017
resume
Cloud computing is becoming the preferred solution for efficiently dealing with the increasing amount of genomic data. Yet, outsourcing storage and processing of sensitive data, such as genomic data, comes with important concerns related to privacy and security. This calls for new sophisticated techniques that ensure data protection from untrusted cloud providers and still enables researchers to obtain useful information. We present a novel privacy-preserving algorithm for fully outsourcing the storage of large genomic data files to a public cloud and enable researchers to efficiently search for variants of interest. To preserve data and query confidentiality from possible leakage, our solution exploits optimal encoding for genomic variants and combines it with homomorphic encryption and private information retrieval. The proposed algorithm is implemented in C++ and evaluated on real data as part of the 2016 iDash genome privacy-protection challenge. Results show that our solution outperforms the state-of-the-art and enables researchers to search over millions of encrypted variants in a few seconds. As opposed to prior beliefs that sophisticated privacy-enhancing technologies (PETs) are unpractical for real operational settings, our solution demonstrates that, in the case of genomic data, PETs can represent very efficient enablers.
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https://hal.science/hal-02381407/file/Efficient%20and%20secure%20outsourcing%20of%20genomic%20data%20storage.pdf BibTex
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