DISTRIBUTED PRIVACY PRESERVING DATA MINING: A FRAMEWORK FOR K-ANONYMITY BASED ON FEATURE SET PARTITIONING APPROACH OF VERTICALLY FRAGMENTED DATABASES
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN ENGINEERING AND TECHNOLOGY
Author(s): JALPA PATEL , KEYUR RANA
Abstract: Recently, many data mining algorithms for discovering and exploiting patterns in data are developed and the amount of data about individuals that is collected and stored continues to rapidly increase. However, databases containing information about individuals may be sensitive and data mining algorithms run on such data sets may violate individual privacy. Also most organizations collect and share information for their specific needs very frequently. In such cases it is important for each organization to make sure that the privacy of the individual is not violated or sensitive information is not revealed. In this paper we have proposed a novel method to provide privacy to the data when the data is vertically partitioned and distributed over sites. In this work we presented trusted third party framework along with an application that generates k-anonymous dataset from two vertically partitioned sources without disclosing data from one site to other. K- anonymity constraint is satisfied
- Publication Date: 25-May-2014
- DOI: 10.15224/978-1-63248-028-6-01-122
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A NOVEL APPROACH USING IMAGE ENHANCEMENT BASED ON GENETIC ALGORITHM
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN ENGINEERING AND TECHNOLOGY
Author(s): PRABHPREET KAUR
Abstract: A robust wavelet domain method for noise filtering in medical images is one of the techniques used to reduce the noise. The method adapts various types of image noise as well as to the preference of the medical expert: a single parameter is being used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. A versatile wavelet domain despeckling technique to visually enhance the medical ultrasound (US) images for improving the clinical diagnosis is used. The method uses the two-sided generalized Nakagami distribution (GND) for modeling the speckle wavelet coefficients and the signal wavelet coefficients are approximated using the generalized Gaussian distribution (GGD) [1]. Combining these statistical priors with the Bayesian maximum a posteriori (MAP) criterion, the thresholding/shrinkage estimators are derived for processing the wavelet coefficients of detail subbands. Consequently, two blind speckle suppressors named as GNDThresh and
- Publication Date: 25-May-2014
- DOI: 10.15224/978-1-63248-028-6-01-123
- Views: 0
- Downloads: 0