Biblio

The increased demand of multimedia leads to shortage of network bandwidth and memory capacity. As a result, image compression is more significant for decreasing data redundancy, saving storage space and bandwidth. Along with the compression the next major challenge in this field is to safeguard the compressed data further from the spy which are commonly known as hackers. It is evident that the major increments in the fields like communication, wireless sensor network, data science, cloud computing and machine learning not only eases the operations of the related field but also increases the challenges as well. This paper proposes a worthy composition for image compression encryption based on unsupervised learning i.e. k-means clustering for compression with logistic chaotic map for encryption. The main advantage of the above combination is to address the problem of data storage and the security of the visual data as well. The algorithm reduces the size of the input image and also gives the larger key space for encryption. The validity of the algorithm is testified with the PSNR, MSE, SSIM and Correlation coefficient.

Cloud computing has transformed the way of utilizing the computing, storage and network resources as per the user demand. Consequently, the field of robotics performs high complexity tasks that exploit the clouds with the capability to build low-cost light weight and intelligent robots. Recently various researchers have been emerged in the cloud robotics field which are related to offloading computations to the cloud infrastructure, storing and sharing knowledge, coordination and collective learning among robots. However, there are issues related to security and privacy that needs to be addressed while deploying the robotics application in the cloud. Significant research attention is required to build a secure cloud robotic infrastructure. The foremost factor of our research entails the development of standard web services that will allow heterogeneous robots to execute the computationally intense algorithms like map building as a service over the cloud. We have proposed the model that presents the mutual authentication and encryption mechanism for getting access to the hosted robotic services. For mutual authentication, we have used Kerberos module and ECIES (Elliptic Curve Integrated Encryption Scheme) for data encryption. Moreover, we have also performed the cryptanalysis of the proposed protocol by using a Proverif tool. After the cryptanalysis, it is found that our system can also withstand against various type of attacks.