Security and Privacy in Mobile ML Pipelines

The emergence of the mobile applications that are driven by machine learning (ML) has brought a breakthrough in the world of healthcare, e-commerce, and entertainment with its personalized and smart user experiences. Despite the wide application of ML pipelines in mobile environments, the security and privacy issues that could be raised have become an issue of high concern on a global scale. The storage and processing of data, combined with the deployment of advanced ML models, expose vulnerabilities to risks such as data leakage, adversarial attacks, and unauthorized access. These threats not only compromise user trust but also incur applications with financial and brand damage. The present article deals with the important problems of the safety of mobile ML pipelines, namely adversarial manipulations, insecure storage, and transmitting of data. The vital techniques that can help to work out those problems are described along with the most popular ones, namely secure model storage based on encryption, federated learning for data transfer reduction, and model robustness tactics that will increase adversarial attacks defense. Besides that, new technologies like homomorphic encryption and blockchain are also discussed as ways to secure model updates. Consequently, this paper is involved in the technical and practical levels of the project, and it is meant to show the importance of building secure and privacy-preserving ML pipelines that do not only keep data for user trust but also maintain efficiency. It serves the purpose of opening the way to moving to secure mobile ML solutions in the time of intelligent mobile systems that have started to be of great interest.