Gallery — Project / Lab Report

Modelo de Relatório de Estágio do Instituto Federal Minas Gerais

Now a day every information is stored and shared on the cloud. And so the medical record especially Personal Health Record (PHR) is an important part of health information exchange, that is need to be stored at cloud servers. But there are various privacy problems as personal health information could be discovered to unauthorized people. That need guarantee of the patient control over to their own PHRs, in this method encryption of the PHRs is done before the storage on cloud. But still issues like risks of privacy, efficiency in key administration, flexible access and efficient user administration, have still remained the important challenge toward achieving better, cryptography imposed data access control. In this research development, we develop a mechanism for control of data access to PHRs stored in cloud servers. To achieve this efficient and modular data access control for PHRs, we provide encryption approach for the encryption to each PHR file. For this system method already tried to focus on the multiple data ownership scheme also dividing the users into security domains that highly reduce the key management complication for owners and users. Here the system takes patient privacy as serious issue and guaranteed it by exploiting multi-authority Encryption. Our main aim is not only privacy but also systems scheme try to enable modification of access policies or file attributes, and break-glass access under emergency situations. Our proposed scheme shows Extensive analysis and experimental results are presented for security and efficiency of PHR.

Plantilla para los trabajos de titulación de la carrera de Electrónica y Telecomunicaciones de la Universidad de Cuenca

Report template for students of TU Delft's course TI3115TU Software Engineering and Methods, part of the Minor Computer Science.

Scientific investigation

Modelo seguindo as normas da Fundação Instituto de Administração - FIA

Uniris Blockchain : Season 1 -- A Truly Decentralized and Limitless Network

Linux Distribution

In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model was random forest, which was able to predict Billboard song success with 88% accuracy.