291 research outputs found
Differentially Private State Estimation in Distribution Networks with Smart Meters
State estimation is routinely being performed in high-voltage power
transmission grids in order to assist in operation and to detect faulty
equipment. In low- and medium-voltage power distribution grids, on the other
hand, few real-time measurements are traditionally available, and operation is
often conducted based on predicted and historical data. Today, in many parts of
the world, smart meters have been deployed at many customers, and their
measurements could in principle be shared with the operators in real time to
enable improved state estimation. However, customers may feel reluctance in
doing so due to privacy concerns. We therefore propose state estimation schemes
for a distribution grid model, which ensure differential privacy to the
customers. In particular, the state estimation schemes optimize different
performance criteria, and a trade-off between a lower bound on the estimation
performance versus the customers' differential privacy is derived. The proposed
framework is general enough to be applicable also to other distribution
networks, such as water and gas networks
Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack
Property inference attacks against machine learning (ML) models aim to infer
properties of the training data that are unrelated to the primary task of the
model, and have so far been formulated as binary decision problems, i.e.,
whether or not the training data have a certain property. However, in
industrial and healthcare applications, the proportion of labels in the
training data is quite often also considered sensitive information. In this
paper we introduce a new type of property inference attack that unlike binary
decision problems in literature, aim at inferring the class label distribution
of the training data from parameters of ML classifier models. We propose a
method based on \emph{shadow training} and a \emph{meta-classifier} trained on
the parameters of the shadow classifiers augmented with the accuracy of the
classifiers on auxiliary data. We evaluate the proposed approach for ML
classifiers with fully connected neural network architectures. We find that the
proposed \emph{meta-classifier} attack provides a maximum relative improvement
of over state of the art.Comment: 12 pages, 2022 Trustworthy and Socially Responsible Machine Learning
(TSRML 2022) co-located with NeurIPS 202
Гидравлический удар: медицинский аспект
КРОВЯНОГО ДАВЛЕНИЯ ИЗМЕРЕНИЕБИОФИЗИКАгидравлический уда
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