# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
###### Abstract  
Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regulations such as HIPAA and GDPR seek to guarantee the confidentiality and privacy of personal information. Input and output data of a learning model may include sensitive data that must be protected. Adversaries could intercept and exploit this data to infer more sensitive data or even to determine the structure of the prediction model. To guarantee data privacy, one option could be encrypting data and making inferences over encrypted data. This strategy would be challenging for learning models that now must receive encrypted data, make inferences over encrypted data, and deliver encrypted data. To address this issue, this paper presents a privacy-preserving machine learning approach using Fully Homomorphic Encryption (FHE) for a model that predicts risk levels of suffering a traffic accident. Despite the limitations of experimenting with FHE on machine learning models using a low-performance computer, limitations that are undoubtedly overcome by using high-performance computational infrastructure, we built some encrypted models. Among the encrypted models based on Decision Trees, Random Forests, XGBoost, and Fully Connected Neural Networks (FCNN), the model based on FCNN reached the highest accuracy (80.1%) for the lowest inference time (8.476 s). s +
Footnote †: journal: Applied Sciences  
driver distraction, and aggressive driving after unusual congestion, and the authors in [5] confirmed that road geometric characteristics and traffic flow directly impact the number of traffic accidents in road sections. Other authors [6] have even gone further in determining the severity of a traffic accident using driver and vehicle information, weather and light conditions, and road conditions. Therefore, as the learning models use more data from different sources, they will be more precise in inferring the risk of suffering a traffic accident. For instance, our Risk Level Prediction System (Section 4.1) uses data from five heterogeneous sources: driver and vehicle information, weather conditions, traffic accidents, and road geometric characteristics. Despite the benefits of learning models, some solutions have been discarded because of restrictions on using certain information. For example, solutions in healthcare have been affected by confidentiality regulations regarding patients' medical records [7]. The data often contain sensitive information that must be protected from unauthorized access. Using sensitive information in machine learning models without a data privacy scheme could cause information leakage and associated consequences. Adversaries can intercept sensitive information such as the vehicle location, timestamp, or driver's ID and determine routes and schedules used by them. Then, we aim to propose a privacy-preserving scheme that guarantees data privacy even in the presence of adversaries for predicting traffic accident risk levels. As is well known, the default mechanism to guarantee data privacy is encryption. Encrypting sensitive information using traditional encryption methods can be effective; however, that information must be deciphered to a certain point to train the models and make inferences, which could result in a serious data privacy issue. In that sense, Homomorphic Encryption (HE) provides a solution to ensure the privacy of both information and models. Based on this requirement, the Privacy-Preserving Machine Learning (PPML) research area has gained interest in the machine learning community. Our proposal consists of a privacy-preserving scheme for a system that includes a learning model that predicts the risk levels of suffering a traffic accident, which is built under an adversary model and uses Homomorphic Encryption. The adversary model identifies and describes all adversaries who could take advantage of capturing sensitive information.