Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

We demonstrate that differentially private machine learning has not yet reached its" AlexNet
moment" on many canonical vision tasks: linear models trained on handcrafted features …

Differential privacy is a strong notion for privacy that can be used to prove formal
guarantees, in terms of a privacy budget, ε, about how much information is leaked by a …

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of
development of information and communication technologies (ICT). With the provision of …

We study the data deletion problem for convex models. By leveraging techniques from
convex optimization and reservoir sampling, we give the first data deletion algorithms that …

A key tool for building differentially private systems is adding Gaussian noise to the output of
a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution …

Some machine learning applications involve training data that is sensitive, such as the
medical histories of patients in a clinical trial. A model may inadvertently and implicitly store …

Abstract Machine learning models trained on private datasets have been shown to leak their
private data. Recent work has found that the average data point is rarely leaked---it is often …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …