SZ El Mestari, G Lenzini, H Demirci - Computers & Security, 2024 - Elsevier
The wide adoption of Machine Learning to solve a large set of real-life problems came with the need to collect and process large volumes of data, some of which are considered …
The newly emerged machine learning (eg, deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
Machine learning (ML) is increasingly being adopted in a wide variety of application domains. Usually, a well-performing ML model relies on a large volume of training data and …
E De Cristofaro - IEEE Security & Privacy, 2021 - ieeexplore.ieee.org
This article reviews privacy challenges in machine learning and provides a critical overview of the relevant research literature. The possible adversarial models are discussed, a wide …
HC Tanuwidjaja, R Choi, S Baek, K Kim - IEEE Access, 2020 - ieeexplore.ieee.org
The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service,(MLaaS) which leverages deep learning …
Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has gained momentum in the recent past. Assuming a minimal network of pair-wise private …
Most current approaches for protecting privacy in machine learning (ML) assume that models exist in a vacuum, when in reality, ML models are part of larger systems that include …
Machine learning algorithms based on deep Neural Networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand …
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 …