# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 8 Conclusions and Future Work  
We designed a privacy-preserving scheme for a traffic accident risk level prediction system that guarantees information confidentiality by making it unreadable for adversaries who can capture that information through an attack. This scheme is based on Fully Homomorphic Encryption (FHE). It guarantees that incoming and outgoing data of the prediction system are IND-CPA secure even if an attacker intercepts its data. Considering the adversary model, our privacy-preserving scheme protects the system from adversaries located in vehicular and cloud service provider networks and the Internet, as established in secrecy lemmas and proofs.  
The high computational cost of performing operations over encrypted data affected the results, especially those related to inference time, which was as expected in line with the Section 4.3.3. These results allowed us to determine the weaknesses and challenges of implementing FHE. Despite these limitations, it is entirely feasible to implement FHE over learning models. Implementing FHE on a high-performance server will dramatically decrease the value of the key performance metrics. This scenario will even permit looking for the model configuration that offers the best performance without worrying about efficiency (shortest times).  
Future work must include evaluating the privacy-preserving scheme using other specialized libraries (e.g., Seal and OpenFHE). Additionally, this evaluation must be performed in a production environment provided by a cloud service provider to confirm the real values of the evaluation metrics. This evaluation will provide information to determine how far the complexity of the learning model, at the expense of efficiency, is relevant. In other words, if it is necessary to sacrifice the complexity of the learning model by getting low response times.  
Finally, we plan to implement federated learning on cloud service providers (e.g., Amazon Web Service and Google Clouds) as an alternative for preserving data privacy. The idea is that data vehicle and driver's data, which include sensitive information such as vehicle and driver's ID and location, are processed into the acquisition modules. Information from the other data sources (weather conditions, traffic accidents, and road geometric characteristics), which do not include sensitive information and are public access, are processed from the centralized server.  
Conceptualization, P.M. and G.S.; methodology, P.M. and G.S.; investigation, P.M.; writing--original draft preparation, P.M.; writing--review and editing, P.M., G.S. and M.H.-A.; supervision, G.S. and M.H.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Our recognition to VIIV (Vicerrectorado de Investigacion, Innovacion y Vinculacion) of Escuela Politecnica Nacional.  
The authors declare no conflicts 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.