Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection

TL;DR

This paper presents a novel deep-learning frequency-domain watermarking technique to protect energy system time series data, ensuring data authenticity and security during sharing or trading.

Contribution

It introduces a new neural network-based watermarking model with specialized loss functions and a frequency-domain preprocessing method for enhanced performance.

Findings

Effective watermark invisibility and robustness demonstrated.

High data restoration accuracy achieved.

Method successfully applied to real energy datasets.

Abstract

Data has been regarded as a valuable asset with the fast development of artificial intelligence technologies. In this paper, we introduce deep-learning neural network-based frequency-domain watermarking for protecting energy system time series data assets and secure data authenticity when being shared or traded across communities. First, the concept and desired watermarking characteristics are introduced. Second, a deep-learning neural network-based watermarking model with specially designed loss functions and network structure is proposed to embed watermarks into the original dataset. Third, a frequency-domain data preprocessing method is proposed to eliminate the frequency bias of neural networks when learning time series datasets to enhance the model performances. Last, a comprehensive watermarking performance evaluation framework is designed for measuring its invisibility, restorability, robustness, secrecy, false-positive detection, generalization, and capacity. Case studies based on practical load and photovoltaic time series datasets demonstrate the effectiveness of the proposed method.