Organizations with a large user base, such as Samsung and Google, can potentially benefit
from collecting and mining users' data. However, doing so raises privacy concerns, and risks …

Because learning sometimes involves sensitive data, machine learning algorithms have
been extended to offer differential privacy for training data. In practice, this has been mostly …

Releasing high-dimensional data enables a wide spectrum of data mining tasks. Yet,
individual privacy has been a major obstacle to data sharing. In this paper, we consider the …

Since big data becomes a main impetus to the next generation of IT industry, data privacy
has received considerable attention in recent years. To deal with the privacy challenges …

Privacy preserving in machine learning is a crucial issue in industry informatics since data
used for training in industries usually contain sensitive information. Existing differentially …

In recent years, deep learning achieves remarkable results in the field of artificial
intelligence. However, the training process of deep neural networks may cause the leakage …

In local differential privacy (LDP), each user perturbs her data locally before sending the
noisy data to a data collector. The latter then analyzes the data to obtain useful statistics …

Nowadays, differential privacy (DP) has become a well-accepted standard for privacy
protection, and deep neural networks (DNN) have been immensely successful in machine …