Informatics Publishing Limited

Mohit Kumar ¹, S. P. Ghrera ²

Affiliations
1 Department of Computer Science and Engineering, Cambridge Institute of Technology, Tatisilwai, Ranchi (Jharkhand), IN
2 Department of Computer Science and Information Technology, Jaypee University of Information Technology, Waknaghat, Distt. Solan, (H.P), IN

Abstract

In this paper we have proposed and successfully implemented a new group key transfer protocol based on CBU hash function. The proposed scheme relies on mutually trusted Key Distribution Center (KDC) to generate and distribute session keys to all communicating entities secretly. In this scheme the key information is broadcasted at once to all the participating entities, but with the available information only the authorized user will be able to recover the actual session key. The advantage of this protocol lies in the fact that even if the pre-shared master key gets compromised, the attacker will still will not be able to recover the actual session key. Furthermore, our protocol makes use of CBU hash function along with Advanced Encryption Standards (AES) to provide confidentiality.

Keywords

Group Key Transfer Protocol, CBU Hash Function, Key Distribution Centre (KDC), Advance Encryption Standards (AES), Confidentiality

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Ashwani Kumar ¹, S. P. Ghrera ², Vipin Tyagi ³

Affiliations
1 1Department of Computer Science, Jaypee University of Information Technoloy (JUIT), Waknaghat - 173234, Solan, Himachal Pradesh, IN
2 Department of Computer Science, Jaypee University of Information Technoloy (JUIT), Waknaghat - 173234, Solan, Himachal Pradesh, IN
3 Department of Computer Science, Jaypee University of Engineering and Technology (JUET), Raghogarh, Guna - 473226. Madhya Pradesh, IN

Abstract

This digital watermarking protocol is basically applied to preserve the rights for both parties involving in E-commerce i.e. buyer and seller. Principal component analysis is used to reduce the correlation coefficient. This protocol uses wavelet transform with principal component analysis and integrates digital watermarking techniques and public key cryptosystem for proving the ownership of digital content. In this paper a watermark image that i.e. baby image is inserted into selected high frequency sub-bands of discrete wavelet transformed. Then we have applied the principal component analysis transformation for selecting the blocks. The process for selecting the blocks are depends by calculating the energy of every block and then the maximum energy blocks were selected. We have calculated parameters such as PSNR and NCC for checking the imperceptibility and robustness of the proposed approach.

Keywords

Copyright Protection, Cryptography, Multimedia, Principal Component Analysis

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Reema Aswani ¹, S. P. Ghrera ², Satish Chandra ³

Affiliations
1 Department of CSE, Ajay Kumar Garg Engineering College, Ghaziabad - 201009, Uttar Pradesh, IN
2 Department of CSE and IT, Jaypee University of Information Technology, Waknaghat -173234, Himachal Pradesh, IN
3 Department of CSE and IT, Jaypee Institute of Information Technology, Noida - 201301, Uttar Pradesh, IN

Abstract

Objectives: Detecting dataset anomalies has been an interesting yet challenging area in this front. This work proposes a hybrid model using meta-heuristics to detect dataset anomalies efficiently. Methods/Statistical Analysis: A distance based modified grey wolf optimization algorithm is designed which uses the k- Nearest Neighbor algorithm for better results. The proposed approach works well with supervised datasets and gives anomalies with respect to each attribute of the dataset based on a threshold using a confidence interval. Findings: The proposed approach works well with regression as well as classification datasets in the supervised scenario. The results in terms of number of anomalies and the accuracy using confusion matrix are depicted and have been evaluated for a classification dataset considering a minority class to be anomalous in comparison to the majority class. The results have been evaluated based on varying the threshold and ‘k’ values and depending on the data set domain and data distribution the optimal parameters can be identified and used. Application/Improvements: The proposed approach can be used for anomaly detection of datasets of different domains of supervised scenario. It can also be extended to unsupervised scenario by integrating it with K-means clustering.

Keywords

Data Mining, Grey Wolf Optimization, k-Nearest Neighbor, Machine Learning, Outlier Detection.

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Amit Kumar Singh ¹, S. P. Ghrera ¹, Vipin Tyagi ¹, Amrit Agrawal ²

Affiliations
1 Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, IN
2 Apollo Institute of Technology, Kanpur, UP, IN

Abstract

Biometrics is becoming an essential component of effective person identification solutions because biometric identifiers cannot be shared or misplaced, and they intrinsically represent the individual's bodily identity. Recognition of a person by their body, then linking that body to an externally established “identity”, forms a very powerful tool of identity management with tremendous potential consequences, both positive and negative. Consequently, biometrics is not only a fascinating pattern recognition research problem but, if carefully used, is an enabling technology with the potential to make our society safer, reduce fraud and provide user convenience. In this paper, a brief overview of biometric system. Comparison of Biometric Technologies and different application of the systems is given.

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