Research On Data Differential Privacy Based On Intelligent Medical Application

With the rapid development of smart medicine and the increasing popularity of information networks,the research on privacy protection of network data release has received widespread attention in recent years.Medical data has become a valuable data asset and has been continuously developed and utilized,which has greatly promoted the improvement of medical service level.However,publishing and using user data will make users vulnerable to reasoning attacks.Due to the particularity of the medical field,medical data not only carries the health status and medical process information of patients,but also involves a large number of individual sensitive information of patients.Malicious attackers with relevant background knowledge may use data mining tools to randomly or frequently collect and analyze specific information,which may cause personal privacy data to be disclosed.This thesis introduces the research status of differential privacy protection model and differential privacy protection method oriented to network graph structure,analyzes and compares the advantages and disadvantages of different privacy protection scheme models,and proposes a differential privacy protection scheme for medical network data.This scheme is based on community density aggregation,combined with edge connection probability perturbation,and introduced correlation coefficient k to ensure the differential privacy and availability of edge related data,and finally publish the network diagram.The experimental results show that compared with the existing classical schemes,the scheme proposed in this thesis has high availability while protecting privacy.As the main carrier of information in the development of smart medicine,medical data plays an important role.The establishment of a large number of medical institutions provides convenience for patients to seek medical treatment,followed by the exponential growth of medical data.However,smart medicine is in the early stage of development.The traditional medical system has problems such as poor inter-institutional interoperability,and the centralized database is vulnerable to "single point of failure".How to achieve efficient storage and safe sharing of medical data has become one of the current research hotspots.The rise of blockchain technology in recent years has provided new solutions to the above problems.This thesis proposes a blockchain-based smart medical data privacy processing scheme.In this scheme,the decentralized distributed system is realized by combining blockchain technology and IPFS.The medical data is stored in IPFS,and the returned file storage address is uploaded to the blockchain system.The medical data is "stored off the chain and indexed on the chain",ensuring the integrity,security and tamper-proof of of the data.Secondly,Laplace noise is added to the statistical patient medical data using the differential privacy model,which reduces the risk of patient privacy information disclosure while preserving the scientific research value of medical data.On this basis,this thesis implements a prototype system of smart medical data security processing scheme based on blockchain,which has certain theoretical and practical value for future practical applications.