SEQUENTIAL GAME NETWORK (SEGANE) WITH APPLICATION TO ONLINE DATA SANITIZATION
Zahir Alsulaimawi, Jinsub Kim, Thinh P. Nguyen · 2018
This paper proposes SEquential GAme NEtwork (SEGANE), a novel deep neural network (DNN) architecture for optimizing the performance of machine learning applications with multiple competing objectives. Specifically, SEGANE is evaluated in the context of data sanitization which aims to remove any pre-specified private information from the data in real time while keeping the relevant information used to improve the inference accuracy about the non-private information. In some settings, preserving private information and improving inference performance about non-private information are competing objectives. In such cases, SEGANE provides a sequential game framework and algorithmic tools to implement data sanitization schemes with flexible trade-off between these two objectives. We use two datasets: MNIST (hand-written digits) and IMDB (gender and age) to evaluate SEGANE. For MNIST, even numbers are considered private while numbers larger than 10 are considered non-private. For IMDB, in one setting, gender is considered private while age is non-private, and vice versa in another setting. Our experimental results on these datasets show that SEGANE is highly effective in removing private information from the dataset while allowing non-private data to be mined effectively.