25 citations · 45 across the 23 of their papers we have counts for
25 papers
Differentially Private Synthetic Voltage Phasor Release for Distribution Grids
Andrew Campbell, Chenyue Zhang, Anna Scaglione +3
Training machine learning models, including Grid Foundation Models (GFMs), requires large volumes of realistic grid data, yet substantial privacy concerns discourage utilities and…
Differential Privacy of Network Parameters from a System Identification Perspective
Andrew Campbell, Anna Scaglione, Hang Liu +3
This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst…
Decentralized Differentially Private Power Method
Andrew Campbell, Anna Scaglione, Sean Peisert
We propose a novel Decentralized Differentially Private Power Method (D-DP-PM) for performing Principal Component Analysis (PCA) in networked multi-agent settings. Unlike conventio…
Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Hang Liu, Anna Scaglione, Sean Peisert
Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, d…
Differentially Private Communication of Measurement Anomalies in the Smart Grid
Nikhil Ravi, Anna Scaglione, Sean Peisert +1
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data. Our DP approach conceals c…
Differential Privacy for Class-based Data: A Practical Gaussian Mechanism
Raksha Ramakrishna, Anna Scaglione, Tong Wu +2
In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be prote…
Solar Photovoltaic Systems Metadata Inference and Differentially Private Publication
Nikhil Ravi, Anna Scaglione, Julieta Giraldez +3
Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations. Still, they are reluctant to share them, as even sharing aggre…
Perspectives for self-driving labs in synthetic biology
Hector Garcia Martin, Tijana Radivojevic, Jeremy Zucker +19
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs co…
Adaptive Control of Distributed Energy Resources for Distribution Grid Voltage Stability
Daniel Arnold, Shammya Saha, Sy-Toan Ngo +5
Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits.…
Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation
Daniel Arnold, Sy-Toan Ngo, Ciaran Roberts +3
Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltag…
Differential Privacy in Aggregated Mobility Networks: Balancing Privacy and Utility
Ammar Haydari, Chen-Nee Chuah, Michael Zhang +2
Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practi…
Differentially Private -means Clustering Applied to Meter Data Analysis and Synthesis
Nikhil Ravi, Anna Scaglione, Sachin Kadam +5
The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholde…
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Yize Chen, Yuanyuan Shi, Daniel Arnold +1
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with…
Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…
Colored Noise Mechanism for Differentially Private Clustering
Nikhil Ravi, Anna Scaglione, Sean Peisert
The goal of this paper is to propose and analyze a differentially private randomized mechanism for the -means query. The goal is to ensure that the information received about th…
Optimum Noise Mechanism for Differentially Private Queries in Discrete Finite Sets
Sachin Kadam, Anna Scaglione, Nikhil Ravi +3
The Differential Privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution mod…
Understanding the Safety Requirements for Learning-based Power Systems Operations
Yize Chen, Daniel Arnold, Yuanyuan Shi +1
Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of…
Lyapunov Stability of Smart Inverters Using Linearized DistFlow Approximation
Shammya Shananda Saha, Daniel Arnold, Anna Scaglione +4
Fast-acting smart inverters that utilize preset operating conditions to determine real and reactive power injection/consumption can create voltage instabilities (over-voltage, volt…
Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments
Ayaz Akram, Anna Giannakou, Venkatesh Akella +2
Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provid…
Deep Reinforcement Learning for DER Cyber-Attack Mitigation
Ciaran Roberts, Sy-Toan Ngo, Alexandre Milesi +7
The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption,…
Catch Me If You Can: Using Power Analysis to Identify HPC Activity
Bogdan Copos, Sean Peisert
Monitoring users on large computing platforms such as high performance computing (HPC) and cloud computing systems is non-trivial. Utilities such as process viewers provide limited…
Trusted CI Experiences in Cybersecurity and Service to Open Science
Andrew Adams, Kay Avila, Jim Basney +16
This article describes experiences and lessons learned from the Trusted CI project, funded by the US National Science Foundation to serve the community as the NSF Cybersecurity Cen…
Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
Mahdi Jamei, Anna Scaglione, Sean Peisert
A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is obse…
Anomaly Detection Using Optimally-Placed Micro-PMU Sensors in Distribution Grids
Mahdi Jamei, Anna Scaglione, Ciaran Roberts +4
As the distribution grid moves toward a tightly-monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the o…