We propose a scheme for auditing differentially private machine learning systems with a
single training run. This exploits the parallelism of being able to add or remove multiple …

Differential privacy is a de facto privacy framework that has seen adoption in practice via a
number of mature software platforms. Implementation of differentially private (DP) …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Motivated by the security risks of deep neural networks, such as various membership and
attribute inference attacks, differential privacy has emerged as a promising approach for …

Differential privacy (DP) provides formal guarantees that the output of a database query
does not reveal too much information about any individual present in the database. While …

We present DP-Sniper, a practical black-box method that automatically finds violations of
differential privacy. DP-Sniper is based on two key ideas:(i) training a classifier to predict if …

We present formal models of the associative and causal views of differential privacy. Under
the associative view, the possibility of dependencies between data points precludes a …

We propose CheckDP, an automated and integrated approach for proving or disproving
claims that a mechanism is differentially private. CheckDP can find counterexamples for …

Differential privacy is an information theoretic constraint on algorithms and code. It provides
quantification of privacy leakage and formal privacy guarantees that are currently …

摘要批处理, 流式计算和机器学习等分布式的大数据计算环境在云上的广泛部署与应用,
为云用户带来了极大的便利, 但随之带来的隐私数据泄露事件愈演愈烈. 如何在这种云上部署的 …