# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 2 Related Work
They also introduced optimization for data sequence prediction for genotype imputation. After evaluating linear models and their privacy-preserving equivalents, the results show similar performance for both, even using large-scale datasets. [PERSON] et al. [18] proposed a method to optimize the iterations of the gradient descent algorithm. They implemented the method for logistic regression models based on HE for the learning phase. This method considerably reduces the number of iterations of the optimization algorithms, which results in a reduction of time of the learning process of the encrypted model and a reduction of the storage of encrypted data. Finally, the following proposals provide models applied indistinctly to different areas (financial, medical informatics, and computer science). All three proposals [8; 21; 22] use FHE as the type of encryption. Thus, [PERSON] et al. [8] proposed an FHE-based Support Vector Machine (SVM) model that includes efficient techniques for polynomial and sign evaluation. The polynomial evaluation minimizes the multiplicative depth of the circuit, and the sign evaluation improves the sign approximation by proposing a way to randomize the iterations. These techniques have improved the value-wise and bit-wise arithmetic. In addition, [PERSON] et al. [21] proposed an algorithm for an SVM model based on gradient descent with support to FHE. This algorithm was designed for the training phase and to protect both the model and the training data. The encryption scheme for this approach supports computations on real numbers. In addition to work in encryption, several secure schemes have been developed in recent years to predict traffic accidents using non-encryption-based methods. The authors in [23] proposed an approach that involves access control and authentication protocols, ensuring that only authorized entities can access sensitive traffic and vehicle data. Some studies propose using blockchain to establish a decentralized, tamper-proof system for storing and sharing traffic data, which can be leveraged for accident prediction while maintaining integrity and transparency [24]. [PERSON] et al. [25] introduced a federated learning-based framework to ensure privacy in traffic flow prediction by aggregating model parameters rather than sharing raw data. Considering our requirements and the purpose of this work, the analyzed proposals present limitations. Thus, [9; 14; 15; 16; 18; 20] did not include experiments in which encrypted models using Homomorphic Encryption were evaluated and, therefore, did not provide accuracy values. Other proposals [8; 10; 11; 19; 22] did not present accuracy values for the unencrypted models to compare their results. In general, none of the proposals were focused on areas of study such as transportation or similar, specifically road traffic safety. In Refs. [9; 10; 11; 12; 13; 18], the authors did not clearly define the Homomorphic Encryption scheme used, or they mentioned it in general terms. Next, some proposals [7; 13; 17; 21] did not include an adversary model because they aim to apply Homomorphic Encryption to learning models rather than provide a privacy-preserving scheme based on HE. Finally, while the non-encryption-based approaches provide useful mechanisms for ensuring privacy and data security in traffic accident prediction, they face several limitations compared to FHE For instance, methods like access control [23], blockchain [24], and federated learning [25] still expose certain data elements, such as model parameters or metadata, to potential inference attacks. In contrast, FHE offers stronger privacy guarantees by allowing computations to be performed directly on encrypted data, ensuring that sensitive information remains fully encrypted throughout the process without any data exposure. Additionally, FHE mitigates the communication overhead and accuracy degradation challenges seen in federated learning models, providing both superior privacy and efficiency in accident prediction.