Hash Function Based Keyword Searchable Encryption Framework in Cloud Server Using MD5 and MECC

Cloud Computing expands its usability to various fields that utilize data and store it in a common space that is required for computing and the purpose of analysis as like the IoT devices.These devices utilize the cloud for storing and retrieving data since the devices are not capable of storing processing data on its own.Cloud Computing provides various services to the users like the IaaS,PaaS and SaaS.The major drawback that is faced by cloud computing include the Utilization of Cloud services for the storage of data that could be accessed by all the users related to cloud.The use of Public Key Encryptions with keyword search(PEKS)provides security against the untrustworthy third-party search capability on publicly encryption keys without revealing the data’s contents.But the Security concerns of PEKs arise when Inside Keywords Guessing attacks(IKGA),is identified in the system due to the untrusted server presume the keyword in trapdoor.This issue could be solved by using various algorithms like the Certificateless Hashed Public Key Authenticated Encryption with Keyword Search(CL-HPAEKS)which utilizes the Modified Elliptic Curve Cryptography(MECC)along with the Mutation Centred flower pollinations algorithm(CM-FPA)that is used in enhancing the performance of the algorithm using the Optimization in keys.The additional use of Message Digests 5(MD5)hash function in the system enhances the security Level that is associated with the system.The system that is proposed achieves the security level performance of 96 percent and the effort consumed by the algorithm is less compared to the other encryption techniques.

A Fast and Memory-Efficient Approach to NDN Name Lookup

For name-based routing/switching in NDN, the key challenges are to manage large-scale forwarding Tables, to lookup long names of variable lengths, and to deal with frequent updates. Hashing associated with proper length-detecting is a straightforward yet efficient solution. Binary search strategy can reduce the number of required hash detecting in the worst case. However, to assure the searching path correct in such a schema, either backtrack searching or redundantly storing some prefixes is required, leading to performance or memory issues as a result. In this paper, we make a deep study on the binary search, and propose a novel mechanism to ensure correct searching path without neither additional backtrack costs nor redundant memory consumptions. Along any binary search path, a bloom filter is employed at each branching point to verify whether a said prefix is present, instead of storing that prefix here. By this means, we can gain significantly optimization on memory efficiency, at the cost of bloom checking before each detecting. Our evaluation experiments on both real-world and randomly synthesized data sets demonstrate our superiorities clearly