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Image fusion is the hottest research subtopic of image processing. Fusion is basically extraction of best inputs and conveying to the output. For making an image, which is more suitable for segmentation, extraction of features, object recognition and human visual system, Image fusion is frequently used. It combines complimentary information from different image of the same scene in a single image. Image fusion can be performed at four different process levels which are signal, pixel, feature and decision level according to the stage at which levels fusion takes place. This paper provides survey of some of the various existing techniques applied for image fusion and comparative study of all techniques concludes better approach for its future research. In this paper we also try to find it out what are things that were neglected by the researchers, so our main objective was to find out the gaps in existing literature.

BWT, DCT, DWT, DT-CWT, Image fusion , PCA

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A. Maurya, R. Tiwari, "Theoretic Study of Image Fusion Techniques � A Survey," International Journal of Computer Sciences and Engineering, Vol.2, Issue.6, pp.43-49, 2014.

Information hiding for security in data transmission is in the air as long as the conversion of data before sending it. Steganography is the art and the science of hiding information or data by embedding within other. Steganography works by replacing bits of useless or unused data in various files (graphics, sound, text or even floppy disks) through LSB technique and this hidden data can be plain text, cipher text or images. But this technique usually takes more time if encryption is done first for improving its security. The aim of this paper is to hide the data after encrypting it through new approach called K-Matrix, which will provide less time complexity than the existing technique for cryptography.

Image Steganography, K-Matrix, Cryptography, LSB, Enhanced Least Significant Bit, Information hiding

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K.K. ARORA, G. GANDHI, "Enhanced Steganography using K-MATRIX," International Journal of Computer Sciences and Engineering, Vol.2, Issue.6, pp.50-53, 2014.

Combining and analyzing data collected at multiple administrative locations is critical for a wide variety of applications, such as detecting malicious attacks or computing an accurate estimate of the popularity of Web sites. However, legitimate concerns about privacy often inhibit participation in collaborative data analysis. In this paper, we design, implement, and evaluate a practical solution for privacy-preserving data analysis and data publishing among a large number of participants. There is an increasing need for sharing data that contain personal information from distributed databases. For example, in the healthcare domain, a national agenda is to develop the Nationwide Health Information Network (NHIN) to share information among hospitals and other providers, and support appropriate use of health information beyond direct patient care with privacy protection. Privacy preserving data analysis and data publishing has received considerable attention in recent years as promising approaches for sharing data while preserving individual privacy. When the data are distributed among multiple data providers or data owners, two main settings are used for anonymization. One approach is for each provider to anonymize the data independently (anonymize-and-aggregate), which results in potential loss of integrated data utility.

m-Privacy, k-anonymity, l-diversity, Database Management, Heuristic algorithms, Distributed Data Publising, Pruning Strategies

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S. Rathod, B.J. Doddegowda , "m-Privacy Preserving Data Analysis And Data Publising," International Journal of Computer Sciences and Engineering, Vol.2, Issue.6, pp.54-58, 2014.