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Michele Minelli

dblp:179/6839 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 3

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
3 papers
Cryptographic primitives and cryptanalysis · 67% Cryptographic protocols and secure computation · 33%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 8 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic primitives and cryptanalysis
homomorphic encryption
0.622018
Fast Homomorphic Evaluation of Deep Discretized Neural Networks · CRYPTO (3) 2018
FHE Circuit Privacy Almost for Free · CRYPTO (2) 2016
Cryptographic primitives and cryptanalysis › homomorphic encryption
fully homomorphic encryption
0.312018
Fast Homomorphic Evaluation of Deep Discretized Neural Networks · CRYPTO (3) 2018
Cryptographic primitives and cryptanalysis › post-quantum cryptography
lattice-based cryptography
0.312018
Lattice-Based zk-SNARKs from Square Span Programs · CCS 2018
Cryptographic primitives and cryptanalysis
post-quantum cryptography
0.312018
Lattice-Based zk-SNARKs from Square Span Programs · CCS 2018
Cryptographic protocols and secure computation › proof systems
zero-knowledge proofs
0.312018
Lattice-Based zk-SNARKs from Square Span Programs · CCS 2018
Cryptographic protocols and secure computation › proof systems › zero-knowledge proofs › non-interactive zero-knowledge proofs
zk-SNARK
0.312018
Lattice-Based zk-SNARKs from Square Span Programs · CCS 2018
Machine learning › Deep learning architectures and training
neural network inference
0.112018
Fast Homomorphic Evaluation of Deep Discretized Neural Networks · CRYPTO (3) 2018
Cryptographic protocols and secure computation › proof systems
square span program
0.112018
Lattice-Based zk-SNARKs from Square Span Programs · CCS 2018

Methods — techniques the papers use, named apart from their topics

lattice assumptions · 0.3LWE · 0.3
YearPublicationVenuePosition
2018 Lattice-Based zk-SNARKs from Square Span Programs
abstract
Zero-knowledge SNARKs (zk-SNARKs) are non-interactive proof systems with short and efficiently verifiable proofs. They elegantly resolve the juxtaposition of individual privacy and public trust, by providing an efficient way of demonstrating knowledge of secret information without actually revealing it. To this day, zk-SNARKs are being used for delegating computation, electronic cryptocurrencies, and anonymous credentials. However, all current SNARKs implementations rely on pre-quantum assumptions and, for this reason, are not expected to withstand cryptanalitic efforts over the next few decades. In this work, we introduce the first designated-verifier zk-SNARK based on lattice assumptions, which are believed to be post-quantum secure. We provide a generalization in the spirit of Gennaro et al. (Eurocrypt'13) to the SNARK of Danezis et al. (Asiacrypt'14) that is based on Square Span Programs (SSPs) and relies on weaker computational assumptions. We focus on designated-verifier proofs and propose a protocol in which a proof consists of just 5 LWE encodings. We provide a concrete choice of parameters as well as extensive benchmarks on a C implementation, showing that our construction is practically instantiable.
Rosario Gennaro, Michele Minelli, Anca Nitulescu, Michele Orrù
CCS2
2018 Fast Homomorphic Evaluation of Deep Discretized Neural Networks
Florian Bourse, Michele Minelli, Matthias Minihold, Pascal Paillier
CRYPTO (3)2
2016 FHE Circuit Privacy Almost for Free
Florian Bourse, Rafaël Del Pino, Michele Minelli, Hoeteck Wee
CRYPTO (2)3