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 …

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 …

Modeling the probability distribution of rows in tabular data and generating realistic synthetic
data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous …

Crowdsourcing is a computing paradigm where humans are actively involved in a
computing task, especially for tasks that are intrinsically easier for humans than for …

The objective of differential privacy (DP) is to protect privacy by producing an output
distribution that is indistinguishable between any two neighboring databases. However …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

Privacy-preserving data publishing is an important problem that has been the focus of
extensive study. The state-of-the-art solution for this problem is differential privacy, which …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

\epsilon-differential privacy is the state-of-the-art model for releasing sensitive information
while protecting privacy. Numerous methods have been proposed to enforce epsilon …

Differential privacy is becoming a gold standard notion of privacy; it offers a guaranteed
bound on loss of privacy due to release of query results, even under worst-case …