Differentially Private <i>K</i>-Means Clustering Applied to Meter Data Analysis and Synthesis-Reference-Cited by-同舟云学术

Differentially Private K-Means Clustering Applied to Meter Data Analysis and Synthesis

Author:

Ravi Nikhil¹^ORCID,Scaglione Anna¹,Kadam Sachin²,Gentz Reinhard³^ORCID,Peisert Sean⁴^ORCID,Lunghino Brent⁵,Levijarvi Emmanuel⁶,Shumavon Aram⁷

Affiliation:

1. Department of Electrical and Computer Engineering, Cornell Tech, New York City, NY, USA

2. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

3. Computational Research, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

4. Computer Sciences Research and Development Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

5. Data Science and Methodology Implementation, Kevala, Inc., San Francisco, CA, USA

6. Software Engineering Department, Kevala, Inc., San Francisco, CA, USA

7. Kevala, Inc., San Francisco, CA, USA

Funder

Director, Cybersecurity, Energy Security, and Emergency Response, Cybersecurity for Energy Delivery Systems Program of the U.S. Department of Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

Reference36 articles.

5. Calibrating noise to sensitivity in private data analysis;dwork;Proc Theory Cryptogr Conf,2006

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