US 12,346,479 B2
Privacy preservation of data over a shared network
Ayse Parlak, Princeton, NJ (US); and Leandro Pfleger de Aguiar, Robbinsville, NJ (US)
Assigned to Siemens Aktiengesellschaft, Munich (DE)
Filed by Siemens Aktiengesellschaft, Munich (DE)
Filed on May 16, 2022, as Appl. No. 17/745,454.
Claims priority of provisional application 63/188,569, filed on May 14, 2021.
Prior Publication US 2022/0366083 A1, Nov. 17, 2022
Int. Cl. G06F 21/62 (2013.01); G06N 3/0455 (2023.01); H04L 9/40 (2022.01)
CPC G06F 21/6254 (2013.01) [G06N 3/0455 (2023.01); H04L 63/0428 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A computer-based method for preserving privacy of shared data across a shared network, comprising:
transforming, by a vector encoder, input data into a feature vector;
transforming, by a neural network-based encoder of a trained autoencoder, the feature vector into anonymized data comprising a fixed size latent space representation of the input data,
transmitting the anonymized data to a trusted party over a shared network,
reconstructing by a neural network-based decoder of the trained autoencoder used by the trusted party, the feature vector from the anonymized data comprising the latent space representation; and
transforming, by a vector decoder used by the trusted party, the reconstructed feature vector into reconstructed data,
wherein the autoencoder including the neural network-based encoder and the neural net-work-based decoder is trained using training data with an objective of minimizing reconstruction error.