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 …
J Zhang, G Cormode, CM Procopiuc… - ACM Transactions on …, 2017 - dl.acm.org
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 …
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 …
X Ren, CM Yu, W Yu, S Yang, X Yang… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the …
With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived …
In differential privacy (DP), a challenging problem is to generate synthetic datasets that efficiently capture the useful information in the private data. The synthetic dataset enables …
J Zhang, X Xiao, X Xie - … of the 2016 international conference on …, 2016 - dl.acm.org
Given a set D of tuples defined on a domain Omega, we study differentially private algorithms for constructing a histogram over Omega to approximate the tuple distribution in …
R McKenna, D Sheldon… - … Conference on Machine …, 2019 - proceedings.mlr.press
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new …