Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection

Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images increases the speed and accuracy of determining COVID-19 cases. However, due to Health Insurance Portability and Accountability (HIPAA) compliance, the hospitals were unwilling to share patient data due to privacy concerns. To maintain privacy, we propose differential private deep learning models to secure the patients' private information. The dataset from the Kaggle website is used to evaluate the designed model for COVID-19 detection. The EfficientNet model version was selected according to its highest test accuracy. The injection of differential privacy constraints into the best-obtained model was made to evaluate performance. The accuracy is noted by varying the trainable layers, privacy loss, and limiting information from each sample. We obtained 84\% accuracy with a privacy loss of 10 during the fine-tuning process

Towards a Dynamic Edge AI Framework applied to autonomous driving cars

[EN] This work proposes an innovative solution in the field of Edge AI in order to efficiently exploit new hardware components available on the market at low cost. Edge AI means that algorithms are processed locally on a hardware device. The algorithms use data (sensor data or signals) that are created on the own device. The idea of this paper focuses on demonstrating the validity of the proposed solution by implementing an autonomous driving system that exploits communication between intelligent agents. In this case, our self-driving cars are equipped with a low-cost device that allows you to recognise objects along the way and consequently take actions by running a machine learning model. The presence of a machine learning model also allows the developer to modify it by extending the flexibility and application possibilities of the proposed solution.This work was partly supported by: ERASMUS+ Programme, KA1 Istruzione Superiore, Carta Erasmus+: 29388-EPP-1-2014-1-IT-EPPKA3-ECHE, ACCORDO PER LA MOBILITÀ ERASMUS PER STUDIO - a.a. 2019/2020, Progetto n o 2019-1-IT02-KA103-061203 - CUP: H25J19000080006, Generalitat Valenciana (PROMETEO/2018/002). Universitat Politecnica de Valencia Research Grant PAID-10-19.Muratore, G.; Rincón Arango, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C.; Greco, G.; Fortino, G. (2020). Towards a Dynamic Edge AI Framework applied to autonomous driving cars. Springer. 406-415. https://doi.org/10.1007/978-3-030-51999-5_34S406415Chang, A.: The role of artificial intelligence in digital health. In: Wulfovich, S., Meyers, A. (eds.) Digital Health Entrepreneurship. HI, pp. 71–81. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12719-0_7Yang, L., Henthorne, T.L., George, B.: Artificial intelligence and robotics technology in the hospitality industry: current applications and future trends. In: George, B., Paul, J. (eds.) Digital Transformation in Business and Society, pp. 211–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-08277-2_13Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N.: Artificial intelligence and internet of things for autonomous vehicles. In: Jazar, R.N., Dai, L. (eds.) Nonlinear Approaches in Engineering Applications, pp. 39–68. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18963-1_2Li, H., Ota, K., Dong, M.: Learning iot in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)Alonso, R.S., Sittón-Candanedo, I., Rodríguez-González, S., García, Ó., Prieto, J.: A survey on software-defined networks and edge computing over IoT. In: De La Prieta, F., et al. (eds.) PAAMS 2019. CCIS, vol. 1047, pp. 289–301. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24299-2_25Wang, T., Mei, Y., Jia, W., Zheng, X., Wang, G., Xie, M.: Edge-based differential privacy computing for sensor-cloud systems. J. Parallel Distrib. Comput. 136, 75–85 (2020)Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. arXiv preprint arXiv:1905.10083 (2019)Sittón-Candanedo, I., Alonso, R.S., Corchado, J.M., Rodríguez-González, S., Casado-Vara, R.: A review of edge computing reference architectures and a new global edge proposal. Future Gener. Comput. Syst. 99, 278–294 (2019)Ke, R., Zhuang, Y., Pu, Z., Wang, Y.: A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. arXiv preprint arXiv:2001.00269 (2020)Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M.: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application. IEEE Access 8, 9102–9114 (2020)Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861 (2017)Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.or

Cloud computing issues, challenges, and needs: A survey

Cloud computing represents a kind of computing that is based on the sharing of computing resources instead of possessing personal devices or local servers for handling several applications and tasks. This kind of computing includes three distinguished kinds of services provided remotely for clients that can be accessed by using the Internet. Typically, clients work on paying annual or monthly service fees for suppliers, in order to gain access to systems that work on delivering infrastructure as a service, platforms as a service, and software as a service for any subscriber. In this paper, the usefulness and the abuse of the cloud computing are briefly discussed and presented by highlighting the influences of cloud computing in different areas. Moreover, this paper also presents the kinds and services of cloud. In addition, the security issues that cover the cloud security solution requirements, and the cloud security issues, which is one of the biggest issues in recent years in cloud computing were presented in this paper. The security requirement that needs by the cloud computing covers privacy, lack of user control, unauthorized secondary usage, and finally data proliferation and data flow. Meanwhile, the security issues cover including ownership of device, the trust issue and legel aspects. To overcome the security issues, this paper also presents the solution at the end of this paper