2,867 research outputs found
Variational Inference for Stochastic Block Models from Sampled Data
This paper deals with non-observed dyads during the sampling of a network and
consecutive issues in the inference of the Stochastic Block Model (SBM). We
review sampling designs and recover Missing At Random (MAR) and Not Missing At
Random (NMAR) conditions for the SBM. We introduce variants of the variational
EM algorithm for inferring the SBM under various sampling designs (MAR and
NMAR) all available as an R package. Model selection criteria based on
Integrated Classification Likelihood are derived for selecting both the number
of blocks and the sampling design. We investigate the accuracy and the range of
applicability of these algorithms with simulations. We explore two real-world
networks from ethnology (seed circulation network) and biology (protein-protein
interaction network), where the interpretations considerably depends on the
sampling designs considered
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Analog compute-in-memory (CIM) accelerators are becoming increasingly popular
for deep neural network (DNN) inference due to their energy efficiency and
in-situ vector-matrix multiplication (VMM) capabilities. However, as the use of
DNNs expands, protecting user input privacy has become increasingly important.
In this paper, we identify a security vulnerability wherein an adversary can
reconstruct the user's private input data from a power side-channel attack,
under proper data acquisition and pre-processing, even without knowledge of the
DNN model. We further demonstrate a machine learning-based attack approach
using a generative adversarial network (GAN) to enhance the reconstruction. Our
results show that the attack methodology is effective in reconstructing user
inputs from analog CIM accelerator power leakage, even when at large noise
levels and countermeasures are applied. Specifically, we demonstrate the
efficacy of our approach on the U-Net for brain tumor detection in magnetic
resonance imaging (MRI) medical images, with a noise-level of 20% standard
deviation of the maximum power signal value. Our study highlights a significant
security vulnerability in analog CIM accelerators and proposes an effective
attack methodology using a GAN to breach user privacy
- …