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Found 11 papers, 1 papers with code
Differentially Private Communication of Measurement Anomalies in the Smart Grid
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data.
Differential Privacy for Class-based Data: A Practical Gaussian Mechanism
In this paper, we present a notion of differential privacy (DP) for data that comes from different classes.
Solar Photovoltaic Systems Metadata Inference and Differentially Private Publication
Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations.
Perspectives for self-driving labs in synthetic biology
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments.
Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation
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 voltages in distribution systems.
Differentially Private $K$-means Clustering Applied to Meter Data Analysis and Synthesis
It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers.
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
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.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Understanding the Safety Requirements for Learning-based Power Systems Operations
Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments
Scientific computing sometimes involves computation on sensitive data.
Distributed, Parallel, and Cluster Computing Hardware Architecture Cryptography and Security
Catch Me If You Can: Using Power Analysis to Identify HPC Activity
These patterns are reflected in the power consumption of the system and can be used to identify programs running.
