US 11,949,770 B2
Method and system for confidential classification of data
Renaud Sirdey, Cernay-la-Ville (FR); and Sergiu Carpov, Massy (FR)
Assigned to COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES, Paris (FR)
Appl. No. 17/605,390
Filed by COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES, Paris (FR)
PCT Filed Apr. 21, 2020, PCT No. PCT/FR2020/000144
§ 371(c)(1), (2) Date Oct. 21, 2021,
PCT Pub. No. WO2020/217005, PCT Pub. Date Oct. 29, 2020.
Claims priority of application No. 1904280 (FR), filed on Apr. 23, 2019.
Prior Publication US 2022/0224508 A1, Jul. 14, 2022
Int. Cl. H04L 29/06 (2006.01); G06F 21/62 (2013.01); G06N 3/088 (2023.01); H04L 9/00 (2022.01)
CPC H04L 9/008 (2013.01) [G06F 21/6254 (2013.01); G06N 3/088 (2013.01); H04L 2209/42 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A confidential data classification platform comprising:
an artificial neural network cascaded with a classifier, the artificial neural network being configured to be trained during a learning phase on data vectors from a learning database and to transform, in an operational phase, input data vectors into discriminative characteristic vectors, said discriminative characteristic vectors being provided to the classifier and having a size smaller than that of the data vectors; and
a reference base in which are stored, during an initialisation phase of the classifier, reference characteristic vectors, obtained by transforming, using the artificial neural network or a copy thereof, reference data vectors, the reference characteristic vectors being stored in encrypted form by a public key of a homomorphic cryptosystem of a user,
wherein, after the initialisation phase, when the user requests the classification platform to classify an input data vector, the classifier evaluates a classification function in a homomorphic domain, from a discriminative characteristic vector (y) provided by the artificial neural network or the copy thereof, and from the reference characteristic vectors stored in encrypted form in the reference base (Enc(yiref,HE.pk), i=1, . . . , N), and transmits a result of the evaluation to the user.