# Privacy-Preserving and Explainable AI in Industrial Applications
## 2 Privacy Preservation in Industrial AI Applications  
### State of the Art in Privacy-Preserving AI  
In this section, we briefly present various approaches for performing privacy-preserving AI.  
One of the most used solutions in privacy-preserving AI is homomorphic encryption (HE). HE allows users to perform computations on encrypted data, yielding results that are also encrypted (results are identical to those obtained by performing the operations on unencrypted data). This type of encryption is necessary when processing sensitive data (e.g., healthcare data). Homomorphic encryption has been introduced and developed independently from AI, but the large computational overhead limits its real-world usage. Since AI-based methods provide results in near real time, i.e., the computational cost during inference is small, extending AI with HE allows for privacy-preserving data processing, while obtaining results in a reasonable amount of time.  
One of the first notable approaches in using homomorphic encryption with neural networks was proposed by [PERSON] et al. [25]. They developed an approach to process encrypted data using a neural network, ensuring that not only the data are protected, but also the neural network itself (weight values and hyperparameters). Figure 1 illustrates the data exchange between server and client, in an encrypted format.  
HE comes in many forms, and one of them is fully homomorphic encryption (FHE), which refers to a cryptosystem able to support arbitrary computation on ciphertexts. [PERSON] et al. [27] used FHE to implement a private decision tree classification of user data. [PERSON] et al. [28]  
Figure 1: Workflow of privacy-preserving feature-based ECG classification method [26].  
performed a review of homomorphic encryption techniques successfully applied in machine learning, and they also documented an R package implementing a homomorphic scheme. Deep neural networks were also employed in studies with homomorphic encryption. For example, [PERSON] et al. [29] used HE for multiparty machine learning; multiple parties participated in training the deep neural network, while maintaining data privacy.  
A novel homomorphic encryption framework was proposed by [PERSON] et al. [30] to protect the data and the expertise of the algorithm when using cloud computing for model training. For healthcare and bioinformatics applications, [PERSON] et al. [31] reviewed the use of FHE together with machine learning models.  
The main disadvantage of FHE is its large computational cost. To address this aspect, partially homomorphic encryption (PHE) schemes were proposed and used by [PERSON] et al. [32] to transmit encrypted gradients from all learning parties, thus speeding up the training by 25-28%, while maintaining the same level of accuracy in comparison with a classic approach.  
Distributing the training process to multiple servers or decentralized edge devices with local data is a preferred approach to address privacy and scalability issues. This type of training is known as federated learning [33] and enables the collaboration of industrial nodes for training a model without exchanging sensitive data. However, reverse engineering may still be employed to extract from the model sensitive information regarding the datasets [34]. Hence, further research is needed for addressing privacy preservation.  
Cloud-based implementations that can be used to run homomorphic encryption frameworks are available. One of them is Google's Cloud Platform [35], which runs on the same infrastructure as Google Search, YouTube, and Google Drive. Another widely used ML service is made available by Microsoft. Microsoft Azure Machine Learning [36] provides ML services which can also help in deploying models and managing them efficiently.