The privacy of machine learning models has become a significant concern in many
emerging Machine-Learning-as-a-Service applications, where prediction services based on …

The privacy of machine learning models has become a significant concern in many
emerging Machine-Learning-as-a-Service applications, where prediction services based on …

In recent years, machine learning approaches have been widely adopted for many
applications, including classification. Machine learning models deal with collective sensitive …

Training a Deep Learning (DL) model requires proprietary data and computing-intensive
resources. To recoup their training costs, a model provider can monetize DL models through …

Machine learning (ML) has shown its impressive performance in the modern world, and
many corporations leverage the technique of machine learning to improve their service …

Deep learning-based service has received great success in many fields and changed our
daily lives profoundly. To support such service, the provider needs to continually collect data …

In this work, we propose information laundering, a novel framework for enhancing model
privacy. Unlike data privacy that concerns the protection of raw data information, model …

Recent attacks on Machine Learning (ML) models such as evasion attacks with adversarial
examples and models stealing through extraction attacks pose several security and privacy …

In model extraction attacks, adversaries can steal a machine learning model exposed via a
public API by repeatedly querying it and adjusting their own model based on obtained …

Recent work by Jia et al.[1], showed the possibility of effectively computing pairwise model
distances in weight space, using a model explanation technique known as LIME. This …