Hierarchical and Dynamic Elliptic Curve Cryptosystem Based Self-Certified Public Key Scheme for Medical Data Protection PROJECT TITLE :Hierarchical and Dynamic Elliptic Curve Cryptosystem Based Self-Certified Public Key Scheme for Medical Data ProtectionABSTRACT:As our aging population considerably grows, personal health monitoring is changing into an rising service and can be accomplished by large-scale, low-power sensor networks, such as Zigbee networks. However, collected medical information could reveal patient privacy, and should be well protected. We tend to propose a Hierarchical and Dynamic Elliptic Curve Cryptosystem based mostly self-certified public key scheme (HiDE) for medical data protection. To serve a giant quantity of sensors, HiDE provides a hierarchical cluster-based mostly framework consisting of a Backbone Cluster and several Area Clusters. In an Space Cluster, a Secure Access Purpose (SAP) collects medical data from Secure Sensors (SSs) in the sensor network, and transmits the aggregated knowledge to a Root SAP located in the Backbone Cluster. So, the Root SAP will serve a substantial variety of SSs while not establishing separate secure sessions with each SS individually. To provide dynamic secure sessions for mobile SSs connecting SAP, HiDE introduces the Elliptic Curve Cryptosystem based Self-certified Public key theme (ESP) for establishing secure sessions between every combine of Cluster Head (CH) and Cluster Member (CM). In ESP, the CH can issue a public key to a CM, and computes a Shared Session Key (SSK) with that CM without knowing the CM's secrete key. This concept satisfies the Zero Knowledge Proof thus CHs will dynamically build secure sessions with CMs while not managing a CM's secrete keys. Our experiments in realistic implementations and Network Simulation demonstrate that ESP requires less computation and network overhead than the Rivest-Shamir-Adleman (RSA)-primarily based public key theme. Furthermore, security analysis shows keys in ESP are well protected. Therefore, HiDE will shield the confidentiality of sensitive medical data with low computation overhead, and keep acceptable network performance for wireless sensor networks. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Economics of Fighting Botnets: Lessons from a Decade of Mitigation Call for papers [Special Issue on Partial Discharges Detection, Measurement, and Interpretation]
