# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 2 Related Work  
Until a few years ago, the main concerns of authors when building learning models were the availability of data and, later, the management of a large amount of data. Those limitations were solved with the spreading of IoT and the emergence of Big Data; however, the current main concern is the restriction on using sensitive information in the learning models. Thus, much of the recent research is focused on looking for strategies or mechanisms to protect sensitive information used by the learning models. One of them is the implementation of Homomorphic Encryption in machine learning. The proposals related to this topic are spread across different research areas and use different types of Homomorphic Encryption. Table 1 summarizes the most important of the proposals. In Genetics and Genomics, the authors in [10; 11] used HE as the type of encryption in their proposals. Thus, [PERSON] et al. [10] proposed a secure multi-label tumor classification model based on a Homomorphic Encryption scheme. This model uses the softmax activation function and efficient data encryption. [PERSON] et al. [11] proposed a solution to train a HE-based logistic regression model to identify cardiopathies. The homomorphic scheme works with real numbers and uses an encoding method for reducing encrypted storage. In the field of medical informatics, the authors in [12; 13] used HE, and those in [7; 14] used FHE as the type of encryption. Thus, [PERSON] et al. [12] proposed a Homomorphic Encryption scheme for learning models. This scheme allows for creating an encrypted logistic regression-based model optimized for real number computation and large-scale datasets. [PERSON] et al. [13] proposed a privacy-preserving classification model of EEG signals for seizure detection and alcohol predisposition using HE. Their model uses a HE-based encoding method that also works with real numbers. [PERSON] et al. [7] proposed a variant of the Matrix Operation for Randomization and Encryption (MORE) scheme for deep neural networks. Its applicability to deep learning models has focused on resolving digit recognition, blood flow circulation, and coronary angiography classification. In the field of computer science, the authors in [15; 16; 17] used FHE, those in [9; 18; 19] used HE, and those in [20] used Partially Homomorphic Encryption (PHE) as the type of encryption. Thus, [PERSON] et al. [15] proposed a secure and efficient clustering scheme based on K-Means, FHE, and a ciphertext packing technique that permits parallel computation. This scheme preserves privacy in database security, clustering results, and data access patterns to prevent inference attacks. The evaluation of the scheme shows a low computational cost, making it more efficient compared with similar models. [PERSON] et al. [16] proposed an FHE-based secure classification model based on Decision Trees. This model supports encoding for real numbers and uses re-linearization to reduce multiplicative ciphertext and modulus switching techniques for decreasing multiplicative ciphertext modules and decryption noise. [PERSON] et al. [17] proposed a privacy-preserving scheme based on FHE for classifying images using neural networks. This scheme incorporates Rectified Linear Unit (ReLU) and Softmax as activation functions but with approximation methods to evaluate them and bootstrapping of the RNS-CKKS as the resampling technique. They applied the scheme to a RestNet-20 model based on FHE. [PERSON] et al. [9] proposed a Homomorphic Encryption scheme based on non-commutative rings for privacy-preserving machine learning. This scheme uses the conjugacy search problem to guarantee one-way security. They also proposed Homomorphic Encryption over a ring of square matrices, which supports the encryption of real numbers. Regarding the performance, the authors mention that their scheme is efficient at the moment of encryption and decryption of information and performing homomorphic operations. [PERSON] et al. [20] proposed a genotype imputation technique applied to PHE-based linear models.