# 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 experiments on Office and Caltech256 datasets indicated that the proposed methodology, despite ensuring differential privacy-loss bound to be less than equal to 1 and not requiring an access to source data samples, performed better than even existing non-private methods in six out of 12 transfer learning experiments. The stress detection experiments on a real-world biomedical data led to the observation that the transfer and multi-task learning scenario \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Privacy** & **Interpretability** & **Transferability** & **Classification** \\  & **Leakage** & **Measure** & **Measure** & **Accuracy** \\ \hline minimum privacy leakage & \(-3.74\) & \(3.47\) & \(291.84\) & \(0.9647\) \\ transfer and multi-task learning & \(-3.74\) & \(3.47\) & \(291.84\) & \(0.3411\) \\ minimum privacy leakage & \(-3.74\) & \(3.47\) & \(291.84\) & \(0.3411\) \\ source domain classifier & \(0.43\) & \(23.92\) & \(773.36\) & \(0.9619\) \\ maximum interpretability measure & \(0.43\) & \(23.92\) & \(773.36\) & \(0.3602\) \\ source domain classifier & \(0.43\) & \(23.92\) & \(773.36\) & \(0.3602\) \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Results (median values) obtained in stress detection experiments on a dataset consisting of heart rate interval measurements. Figure 7: A display of source domain R-R interval data corresponding to different levels of privacy leakage, interpretability measure, and transferability measure. improved the accuracy from 0.3411 to 0.9647 (while ensuring the minimum privacy leakage) and from 0.3602 to 0.9619 (while ensuring the maximum interpretability measure). The considered unified approach to privacy-preserving interpretable and transferable learning involves membership-mappings-based conditionally deep autoencoders, albeit other data representation learning models could be explored. The future work includes the following:

* Although the text has not focused on federated learning, the transfer learning approach could be easily extended to the multi-party system and the transferability measure could be calculated for any pair of parties. * Also, the explainability of the conditionally deep autoencoders follows, similar to in [17], via estimating interpretable parameters from non-interpretable data feature vectors using variational membership-mapping Bayesian model. * Furthermore, the variational membership-mapping Bayesian model quantifies uncertainties on the estimation of parameters (of interest), which is also important for a user's trust on the model. Conceptualization, M.K. ; methodology, M.K. ; writing--original draft preparation, M.K. ; writing--review and editing, L.F.; project administration, B.F.; funding acquisition, B.A.M. and L.F. All authors have read and agreed to the published version of the manuscript. The research reported in this paper has been supported by the Austrian Research Promotion Agency (FFG) COMET-Modul S3 AI (Security and Safety for Shared Artificial Intelligence); FFG SubProject PETAI (Privacy Secured Explainable and Transferable AI for Healthcare Systems); FFG Grant SMiLe (Secure Machine Learning Applications with Homomorphically Encrypted Data); FFG Grant PRIMAL (Privacy-Preserving Machine Learning for Industrial Applications); and the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry for Digital and Economic Affairs, and the State of Upper Austria in the frame of the SCCH competence center INTEGRATE [(FFG grant no. 892418)] part of the FFG COMET Competence Centers for Excellent Technologies Programme. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript:

TAI Trustworthy Artificial Intelligence

## References

* (1) High-Level Expert Group on AI.