# 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
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 & **89.1** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & 86.9 \\ non-private CDLS & VGG-FC6 & 86.3 \\ non-private MMDT & VGG-FC6 & 77.9 \\ non-private HFA & VGG-FC6 & 87.0 \\ non-private OBTL & SURF & 61.5 \\ non-private ILS (1-NN) & SURF & 56.2 \\ non-private CDLS & SURF & 59.8 \\ non-private MMDT & SURF & 56.5 \\ non-private HFA & SURF & 55.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy (in %, averaged over 20 experiments) obtained in _callech256\(\rightarrow\)dslr_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 & **89.1** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & 86.9 \\ non-private CDLS & VGG-FC6 & 86.3 \\ non-private MMDT & VGG-FC6 & 77.9 \\ non-private HFA & VGG-FC6 & 87.0 \\ non-private OBTL & SURF & 61.5 \\ non-private ILS (1-NN) & SURF & 56.2 \\ non-private CDLS & SURF & 59.8 \\ non-private MMDT & SURF & 56.5 \\ non-private HFA & SURF & 55.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy (in %, averaged over 20 experiments) obtained in _callech256\(\rightarrow\)webcam_ semi-supervised transfer learning experiments. The first and second best performances have been marked. is concerned with the detection of stress in an individual based on the analysis of recorded sequence of R-R intervals, \(\{RR^{i}\}_{i}\). The R-R data vector at \(i-\)th time-index, \(y^{i}\), is defined as

\[y^{i}=[\ RR^{i}\ RR^{i-1}\ \cdots\ RR^{i-d}]^{T}. \tag{119}\]

That is, the current interval and history of previous \(d\) intervals constitute the data vector. Assuming an average heartbeat of 72 beats per minute, \(d\) is chosen as equal to \(72\times 3=216\) so that the R-R data vector consists of on average 3-minute-long R-R intervals sequences. A dataset, say \(\{y^{i}\}_{i}\), is built via (1) preprocessing the R-R interval sequence \(\{RR^{i}\}_{i}\) with an impulse rejection filter [37] for artifacts detection and (2) excluding the R-R data vectors containing artifacts from the dataset. The dataset contains the stress score on a scale from 0 to 100. A label of either \"_no-stress_\" or \"_under-stress_\" is assigned to each \(y^{i}\) based on the stress score. Thus, we have a binary classification problem. #### 6.3.1 Interpretable Parameters

Corresponding to a R-R data vector, there exists the set of interpretable parameters: _mental demand_, _physical demand_, _temporal demand_, _own performance_, _effort_, and _frustration_. These are the six components of stress acquired using the NASA Task Load Index [38]. The NASA Task Load Index provides subjective assessment of stress where an individual provides a rating on the scale from 0 to 100 for each of the six components of stress (mental demand, physical demand, temporal demand, own performance, effort, and frustration). Thus, corresponding to each \(217-\)dimensional R-R data vector, there exists a six-dimensional interpretable parameters vector acquired using the NASA Task Load Index. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Type** & **Accuracy (\%)** \\ \hline privacy-preserving maximum interpretability-measure model & VGG-FC6 & **82.9** \\ privacy-preserving maximum transferability-measure model & VGG-FC6 & **82.9** \\ non-private ILS (1-NN) & VGG-FC6 & 81.4 \\ non-private CDLS & VGG-FC6 & 77.9 \\ non-private MMDT & VGG-FC6 & 71.8 \\ non-private HFA & VGG-FC6 & 74.8 \\ non-private OBTL & SURF & 40.3 \\ non-private ILS (1-NN) & SURF & 41.0 \\ non-private CDLS & SURF & 34.9 \\ non-private MMDT & SURF & 34.1 \\ non-private HFA & SURF & 30.9 \\ \hline \hline \end{tabular}
\end{table}