Privacy-Preserving Algorithm for Medical Data
Yogesh Shilewar, Gurudev Sawarkar, Rahul Bhandekar · International Journal of Innovations in Engineering and Science · 2022
Various mobile applications are emerging as a result of the rapid development of mobile internet and the growing popularity of smart terminals.Medical data has evolved into a valuable asset that is constantly assessed and applied, resulting in a significant improvement in the quality of medical care.However, publishing and using user data exposes the user to the possibility of an attack.Medical data carries not only the patient's medical state and medical knowledge, but also the individual's sensitive personal information of a huge number of patients, due to the unique character of the medical profession.Allowing users to fully benefit from social networks while maintaining security is a critical issue that must be addressed immediately in the age of big data.We begin by providing an overview of the privacy hazards of social network data and several sorts of assaults in this study.We propose a privacy protection algorithm based on privacy privacy to leak confidentiality to sensitive social networks.The system employs edge-based weight conversion, which drastically reduces the calculation value and allows for a quicker response from the user.Reduces user leakage of confidential user data while maintaining personal standards under data availability.This strategy, in comparison to more complex ways, protects users against thinking attacks and eliminates the distortion of standard findings produced by data misunderstanding, ensuring the correctness of the suggestions.Our system can ensure effective and long-term security of usersensitive data, according to real-world data sets Keyword-Medical