The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

Understanding to what extent neural networks memorize training data is an intriguing
question with practical and theoretical implications. In this paper we show that in some …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …

Many companies provide neural network prediction services to users for a wide range of
applications. However, current prediction systems compromise one party's privacy: either the …

Current model extraction attacks assume that the adversary has access to a surrogate
dataset with characteristics similar to the proprietary data used to train the victim model. This …

Abstract Machine learning models deployed as a service (MLaaS) are susceptible to model
stealing attacks, where an adversary attempts to steal the model within a restricted access …

We introduce CryptGPU, a system for privacy-preserving machine learning that implements
all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in …

Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making
even the most complex Machine Learning models available for clients via, eg, a pay-per …