  Protecting sensitive information against data exploiting attacks is an
emerging research area in data mining. Over the past, several different methods
have been introduced to protect individual privacy from such attacks while
maximizing data-utility of the application. However, these existing techniques
are not sufficient to effectively protect data owner privacy, especially in the
scenarios that utilize visualizable data (e.g. images, videos) or the
applications that require heavy computations for implementation. To address
these problems, we propose a new dimension reduction-based method for privacy
preservation. Our method generates dimension-reduced data for performing
machine learning tasks and prevents a strong adversary from reconstructing the
original data. We first introduce a theoretical approach to evaluate dimension
reduction-based privacy preserving mechanisms, then propose a non-linear
dimension reduction framework motivated by state-of-the-art neural network
structures for privacy preservation. We conducted experiments over three
different face image datasets (AT&T, YaleB, and CelebA), and the results show
that when the number of dimensions is reduced to seven, we can achieve the
accuracies of 79%, 80%, and 73% respectively and the reconstructed images are
not recognizable to naked human eyes.
