A privacy-preserving adversarial network (PPAN) was recently proposed as an information-
theoretical framework to address the issue of privacy in data sharing. The main idea of this …

Recently, edge computing has attracted significant interest due to its ability to extend cloud
computing utilities and services to the network edge with low response times and …

Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

Machine Learning (ML) models are ubiquitous in real world applications and are a constant
focus of research. At the same time, the community has started to realize the importance of …

The quality of a machine learning model depends on the volume of data used during the
training process. To prevent low accuracy models, one needs to generate more training data …

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of
striking a balance between the value of data and individual privacy have led to the …

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 …

We explore how data modification can enhance privacy by examining the connection
between data modification and machine learning. Specifically, machine learning “meets” …

Edge computing enables real-time processing and response by storing, analyzing, and
processing data near the source. The surge in the amount of data generated as a result of …

Abstract Social Network Sites (SNS) such as Facebook and Twitter, play a great role in our
lives. On one hand, they help to connect people who would not otherwise be connected …