In cloud computing, a third party hosts a client's data, which raises privacy and security concerns. To maintain privacy, data should be encrypted by cryptographic techniques. However, encrypting the ...data makes it unsuitable for indexing and fast processing, as data needs to be decrypted to plain text before it can be further processed. Homomorphic encryption helps to overcome this shortcoming by allowing users to perform operations on encrypted data without decryption. Many academics have attempted to address the issue of data security, but none have addressed the issue of data privacy in cloud computing as thoroughly as this study has. This paper discusses the challenges involved in maintaining the privacy of cloud-based data and the techniques used to address these challenges. It was identified that homomorphic encryption is the best solution of all. This work also identified and compared the various homomorphic encryption schemes which are capable of ensuring the privacy of data in cloud storage and ways to implement them through libraries.
Cloud computing is a hot technology in the market. It permits user to use all IT resources as computing services on the basis of pay per use manner and access the applications remotely. ...Infrastructure as a service (IaaS) is the basic requirement for all delivery models. Infrastructure as a service delivers all possible it resources (Network Components, Operating System, etc.) as a service to users. From both users and providers point of view: integrity, privacy and other security issues in IaaS are the important concern. In this paper we studied in detail about the different types of security related issues in IaaS layer and methods to resolve them to maximize the performance and to maintain the highest level of security in IaaS.
Image is crucial in determining the gender and emotion of an individual in the digital age; however, the issue is evidently with the current methodologies. Implementing deep learning algorithms into ...image processing would be a more effective strategy. By developing emoticons from the emotions captured in images and snapshots, we hope to bridge the divide in communication. We implemented the Keras framework and evaluated its performance on Tensor Flow utilizing a CNN (Convolutional Neural Network) deep learning algorithm in order to ascertain gender. The objective is to eliminate noise and derive features from the image dataset in order to generate a new dataset that can be utilized to implement CNN. We have utilized the LSTM-RNN (long short-term memory recurrent neural network) to detect emotions and recognize facial expressions. With remarkable accuracy, the algorithm can determine the gender of an individual based on a live webcam feed or an image.
Cloud computing is becoming one of the next IT industry buzz word. However, as cloud computing is still in its infancy, current adoption is associated with numerous challenges like security, ...performance, availability, etc. In cloud computing where infrastructure is shared by potentially millions of users, Distributed Denial of Service (DDoS) attacks have the potential to have much greater impact than against single tenanted architectures. This paper tested the efficiency of a cloud trace back model in dealing with DDoS attacks using back propagation neural network and finds that the model is useful in tackling Distributed Denial of Service attacks.
A Distributed Denial of Service (DDoS) attack try to make services or resources unavailable to legitimate customers of that service or resource. These attacks are relatively easy to perform and ...extremely hard to detect in early stages. Different types of detection techniques were proposed by researcher to detect DDoS in its early stages but due to closed nature of networking devices, no promising results were produced. However, with the advents of Software-Defined Network (SDN), situation has changed. SDN is a cutting edge technology, which separates control plane from data plane. Its features, like programmability and centralized view of network, can be used to overcome the difficulties faced by researches. In this paper, we discussed different detection techniques of DDoS and how these techniques can be made more effective by leveraging the unique features of SDN.
Wireless communication channels experience high variability in channel quality due to a variety of phenomenon, including multipath, fading, atmospheric effects, and obstacles. Due to this different ...wireless communication channels have different capacity for transferring the data. This paper studies the movements of nodes within channel environments using Ricean, Rayleigh and Free space channel models. Three performance metrics are used for this purpose. These metrics are throughput, end to end delay and link ratio. It is discovered that free space channel model performed better than Ricean channel which in turn perform better than Rayleigh channel model.
The capability of lower order Krawtchouk moment-based shape features has been analyzed. The behaviour of 1D and 2D Krawtchouk polynomials at lower orders is observed by varying Region of Interest ...(ROI). The paper measures the effectiveness of shape recognition capability of 2D Krawtchouk features at lower orders on the basis of Jochen-Triesch’s database and hand gesture database of 10 Indian Sign Language (ISL) alphabets. Comparison of original and reduced feature-set is also done. Experimental results demonstrate that the reduced feature dimensionality gives competent accuracy as compared to the original feature-set for all the proposed classifiers. Thus, the Krawtchouk moment-based features prove to be effective in terms of shape recognition capability at lower orders.
In this paper, discrete orthogonal moment-based shape features up to 5th order are proposed for Indian sign language (ISL) recognition system. The shape recognition capability of discrete orthogonal ...moment-based local features is verified on two databases. These include the standard Jochen-Triesch’s database and 26 ISL alphabets. The ISL alphabets are collected on both uniform and complex backgrounds, with variations in position, scale and rotation. The feature-set is increased for 26 ISL alphabets by varying Region of Interest (ROI) and extracting features from each ROI. A minimum possible feature-set with least redundancy is selected that gives the best recognition accuracy. The effect of order and feature dimensionality for different classifiers is studied. Results show that both Dual-Hahn and Krawtchouk moments are found to exhibit user, scale, rotation and translation invariance. Moreover, they have shape identification capability, thus achieving good recognition accuracy.
In this paper, Krawtchouk moment-based shape features at lower orders are proposed for Indian sign language (ISL) recognition system which gives local information about the shape from a specific ...region of interest. The shape recognition capability of Krawtchouk moment-based local features is verified on two databases: the standard Jochen Triesch's database and 26 ISL alphabets which are collected from 72 different subjects, with variations in position, scale and rotation. Feature selection is performed to minimise redundancy. The effect of order and feature dimensionality for different classifiers is studied. Results show that Krawtchouk moment-based local features are found to exhibit user, scale, rotation and translation invariance. Moreover, they have shape identification capability.
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