Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Open banking enables individual customers to own their banking data, which provides
fundamental support for the boosting of a new ecosystem of data marketplaces and financial …

The wide adoption and application of Masked language models~(MLMs) on sensitive data
(from legal to medical) necessitates a thorough quantitative investigation into their privacy …

Recent years have witnessed remarkable progress made in large language models (LLMs).
Such advancements, while garnering significant attention, have concurrently elicited various …

Recent advances in the capacity of large language models to generate human-like text have
resulted in their increased adoption in user-facing settings. In parallel, these improvements …

With the growing demand for the right to be forgotten, there is an increasing need for
machine learning models to forget sensitive data and its impact. To address this, the …

We studied 53 privacy-preserving neural-network papers in 2016-2022 based on
cryptography (without trusted processors or differential privacy), 16 of which only use …