# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 5 Information Theoretic Measures for Privacy Leakage, Interpretability, and Transferability
### A Unified Approach to Privacy-Preserving Interpretable and Transferable Learning
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Type** & **Accuracy (\%)** \\ \hline privacy-preserving maximum interpretability-measure model & VGG-FC6 & 89.3 \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & 89.3 \\ non-private ILS (1-NN) & VGG-FC6 & 90.7 \\ non-private CDLS & VGG-FC6 & **91.2** \\ non-private MMDT & VGG-FC6 & 82.5 \\ non-private HFA & VGG-FC6 & 87.9 \\ non-private OBTL & SURF & 72.4 \\ non-private ILS (1-NN) & SURF & 59.7 \\ non-private CDLS & SURF & 68.7 \\ non-private MMDT & SURF & 64.6 \\ non-private HFA & SURF & 57.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy (in %, averaged over 20 experiments) obtained in _amazon\(\rightarrow\)webcam_ semi-supervised transfer learning experiments. The first and second best performances have been marked. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Type** & **Accuracy (\%)** \\ \hline privacy-preserving maximum interpretability-measure model & VGG-FC6 & **92.6** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & **92.6** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & **92.6** \\ non-private ILS (1-NN) & VGG-FC6 & 89.7 \\ non-private CDLS & VGG-FC6 & 88.0 \\ non-private MMDT & VGG-FC6 & 85.9 \\ non-private HFA & VGG-FC6 & 86.2 \\ non-private OBTL & SURF & 54.8 \\ non-private ILS (1-NN) & SURF & 55.1 \\ non-private MMDT & SURF & 49.4 \\ non-private HFA & SURF & 43.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy (in %, averaged over 20 experiments) obtained in _caltech256\(\rightarrow\)amazon_ semi-supervised transfer learning experiments. The first and second best performances have been marked. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Type** & **Accuracy (\%)** \\ \hline privacy-preserving maximum interpretability-measure model & VGG-FC6 & 88.5 \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & **88.7** \\ non-private CDLS & VGG-FC6 & 86.9 \\ non-private MMDT & VGG-FC6 & 77.1 \\ non-private HFA & VGG-FC6 & 87.1 \\ non-private OBTL & SURF & 60.2 \\ non-private ILS (1-NN) & SURF & 49.8 \\ non-private CDLS & SURF & 60.4 \\ non-private MMDT & SURF & 56.7 \\ non-private HFA & SURF & 55.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy (in %, averaged over 20 experiments) obtained in _amazon\(\rightarrow\)dslr_ semi-supervised transfer learning experiments. The first and second best performances have been marked. ### An Application Example: Mental Stress Detection

The mental stress detection problem is considered as an application example of the proposed privacy-preserving interpretable and transferable learning approach. The dataset from [17], consisting of heart rate interval measurements of different subjects, is considered for the study of an individual stress detection problem. In [17], a membership-mappings-based interpretable deep model was applied for an estimation of stress score; however, the current study deals with application of the proposed privacy-preserving interpretable and transferable deep learning method to solve the stress classification problem. The problem \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Type** & **Accuracy (\%)** \\ \hline privacy-preserving maximum interpretability-measure model & VGG-FC6 & **91.9** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & **91.9** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & **91.9** \\ non-private ILS (1-NN) & VGG-FC6 & 88.7 \\ non-private CDLS & VGG-FC6 & 88.1 \\ non-private MMDT & VGG-FC6 & 83.6 \\ non-private HFA & VGG-FC6 & 85.9 \\ non-private OBTL & SURF & 54.4 \\ non-private ILS (1-NN) & SURF & 55.0 \\ non-private CMDS & SURF & 50.7 \\ non-private MMDT & SURF & 46.9 \\ non-private HFA & SURF & 42.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy (in %, averaged over 20 experiments) obtained in _dslr\(\rightarrow\)amazon_ semi-supervised transfer learning experiments.