Generativer Mechanismus zur Wahrung der Privatsphäre für die Offenlegung von Industriellen Zeitseriendaten

Details

Veröffentlichungs-Nr.

EP4239526

Aktenzeichen

EP22175731

Anmeldetag

27. Mai 2022

Veröffentlichung

6. September 2023

Rechtsraum

EP

IPC

G06N3/04G06N3/08// G06N5/00

Abstract

Existing privacy-preserving techniques suffer from inherent drawbacks to retain characteristics of observed, and original industrial time series data for utility in the downstream tasks such as process modelling, control, optimization and etc. The embodiments herein provide a method and system for privacy preserving generative mechanism for data-disclosure of the industrial multivariate mixed-variable time series data. The system fuses an industrial time series data with a random gaussian noise to preserve the privacy of the industrial time series data and trades-off the privacy with the utility of synthetic-private data. Further, the system presents the privacy-preserving synthetic industrial data generative mechanism for data disclosure with minimal risk of AI technique and strong privacy guarantees. Embedding privacy by design into the generative mechanism approaches present an alternate paradigm of learning in contrast to the reduced-order modeling and numerical solutions of the industrial time-series data based on the principles in continuum mechanics for data disclosure with privacy.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete