Privacy-Preserving Distributed Joint Probability Modeling for Spatial-Correlated Wind Farms

by   Mengshuo Jia, et al.

Building the joint probability distribution (JPD) of multiple spatial-correlated wind farms (WFs) is critical for chance-constrained optimal decision-making. The vertical partitioning historical wind power data of WFs is the premise of training the JPD. However, to protect data privacy, WFs with different stakeholders will refuse to share raw data directly or send raw data to a third party as no one knows whether the third party can be trusted. Moreover, the centralized training way is also faced with costly high bandwidth communication, single-point failure and limited scalability. To solve the problems, distributed algorithm is an alternative. But to the best of our knowledge, rarely has literature proposed privacy-preserving distributed (PPD) algorithm to build the JPD of spatial-correlated WFs. Therefore, based on the additive homomorphic encryption and the average consensus algorithm, we first propose a PPD summation algorithm. Meanwhile, based on the binary hash function and the average consensus algorithm, we then present a PPD inner product algorithm. Thereafter, combining the PPD summation and inner product algorithms, a PPD expectation-maximization algorithm for training the Gaussian-mixture-model-based JPD of WFs is eventually developed. The correctness and the robustness to communicate failure of the proposed algorithm is empirically verified using historical data.


page 1

page 11

page 12


Privacy-Preserving Probabilistic Forecasting for Temporal-spatial Correlated Wind Farms

Adopting Secure scalar product and Secure sum techniques, we propose a p...

Privacy-Preserving Distributed Clustering for Electrical Load Profiling

Electrical load profiling supports retailers and distribution network op...

Privacy Preserving Ultra-Short-term Wind Power Prediction Based on Secure Multi Party Computation

Mining the spatial and temporal correlation of wind farm output data is ...

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation

Privacy has become a major concern in machine learning. In fact, the fed...

FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework

Feature engineering is the process of using domain knowledge to extract ...

Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models

Various privacy-preserving frameworks that respect the individual's priv...

Privacy-Preserving Password Cracking: How a Third Party Can Crack Our Password Hash Without Learning the Hash Value or the Cleartext

Using the computational resources of an untrusted third party to crack a...

Please sign up or login with your details

Forgot password? Click here to reset