Machine learning (ML) explainability is central to algorithmic transparency in high-stakes settings such as predictive diagnostics and loan approval. Yet these same domains demand …
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However, their unpredictable and …
Relying on untrusted data exposes machine learning models to backdoor attacks, where adversaries poison training data to embed hidden behaviors. Existing defenses struggle against …
In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data. Existing MIA defenses …
In modern CPU architectures, last-level caches (LLCs) are typically shared among multiple CPU cores. LLCs enable data sharing across application threads and promote data …
The arms race between Internet freedom advocates and censors has catalyzed the emergence of sophisticated blocking techniques and directed significant research emphasis toward the …
The widespread usage of machine learning (ML) in a myriad of domains has raised questions about its trustworthiness in securitycritical environments. Part of the quest for …
Differential Privacy (DP) has emerged as a rigorous formalism to reason about quantifiable privacy leakage. In machine learning (ML), DP has been employed to limit …
The arms race between Internet freedom technologists and censoring regimes has catalyzed the deployment of more sophisticated censoring techniques and directed significant research …
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection …