In graph theory, the dense subgraph problem aims at finding the densest subgraph for a given graph. The subgraph is defined as a subset of a large graph and the density is calculated as the number of edges as per the number of vertices. Real-life graph networks are complicated and large in size; finding the dense subgraph is difficult as the number of subgraphs is numerous. It is an NP-hard problem and in most cases, it is infeasible to find an exact solution. Here we propose a genetic algorithm approach to solve it. The problem is a combinatorial optimization problem with a constraint that all solutions must be a subgraph of the given graph. Here, the crossover and mutation are designed to produce only feasible solutions. The proposed algorithm is able to give close approximation within a reasonable time.

Data secrecy is a major concern in many domains. Nowadays, the data are kept in tight security with high privacy. Users do not want to share their secret information with anyone; however, the users' confidential data are not protected from the administrators. Administrators can read the users' data. Why should any Administrator read users' data? To address this issue, we propose a new secrecy protocol to store data secretly, named Secret Cloud Storage, SecretStore for short, to enable Secrecy as a Service model over the Cloud Computing paradigm. This paper demonstrates how to protect users' data from any unintended users, including the data administrators. Moreover, we introduce tight security using the client-side symmetric cryptography method. In addition, we devise a forgetful private key to generate or regenerate a private key to encrypt or decrypt based on a secret word. We also show how to strengthen the weak password. Finally, we demonstrate how to implement the Secrecy as a Service model in Cloud Storage using highly unpredictable private keys.