CEDS DP K-Means Clustering

This repository contains the code for the CEDS Data Product K-Means Clustering. It follows the methodology proposed in papers [1] and [2].

[1] Ravi, Nikhil, et al. "Differentially Private-Means Clustering Applied to Meter Data Analysis and Synthesis." IEEE Transactions on Smart Grid 13.6 (2022): 4801-4814.
[2] Ravi, Nikhil, Anna Scaglione, and Sean Peisert. "Colored noise mechanism for differentially private clustering." arXiv preprint arXiv:2111.07850 (2021).

This software implements K-means clustering to power grid advanced metering infrastructure (AMI) data to obtain customer labels and centroids for a set of load time series by applying the framework of differential privacy. This is useful to generate differentially private synthetic load data consistent with the labeled data.

Installation

shell pip install git+https://github.com/lbnl-cybersecurity/CEDSKMeans.git

Centralized Usage

```python from cedskmeans import DPKMeans

Import the data

X = "Import data here in the form of a numpy ndarray"

Create a CEDSKMeans object

kmeans = DPKMeans( nclusters=6, epsilon=0.1, delta=1e-5, maxiter=1000 ) kmeans.fit(X)

Access the labels

labels = kmeans.labels_

Access the centroids

centroids = kmeans.clustercenters

Access the true labels

truelabels = kmeans.truelabels_

Access the true centroids

truecentroids = kmeans.trueclustercenters ```

Distributed Map Reduce Usage (requires ray)

```python from cedskmeans import runkmeanmap_reduce import ray

Import the data

X = "Import data here in the form of a pandas dataframe"

ray.init()

Create a CEDSKMeans object

kmeans = runkmeanmapreduce.remote( X=X, nclusters=3, nmappers=2, maxiter=1000, epsilon=0.1, delta=1e-5, ) kmeans = ray.get(kmeans)

Access the labels

labels = kmeans.labels_

Access the centroids

centroids = kmeans.clustercenters ```

Copyright Notice

CEDS Differential Privacy (CEDSDP) Copyright (c) 2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy) and Cornell University. All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.