Generic CBTS: Correlation based Transformation Strategy for Privacy Preserving Data Mining

Mining useful knowledge from corpus of data has become an important application in many fields. Data Mining algorithms like Clustering, Classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it will affect the Data Mining algorithms in knowledge extraction, so an effective mechanism is required to provide privacy to the data and at the same time without affecting the Data Mining results. Privacy concern is a primary hindrance for quality data analysis. Data mining algorithms on the contrary focus on the mathematical nature than on the private nature of the information. Therefore instead of removing or encrypting sensitive data, we propose transformation strategies that retain the statistical, semantic and heuristic nature of the data while masking the sensitive information. The proposed Correlation Based Transformation Strategy (CBTS) combines Correlation Analysis in tandem with data transformation techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Non Negative Matrix Factorization (NNMF) provides the intended level of privacy preservation and enables data analysis. The proposed technique will work for numerical, ordinal and nominal data. The outcome of CBTS is evaluated on standard datasets against popular data mining techniques with significant success and Information Entropy is also accounted

Conditioned Medium Reconditions Hippocampal Neurons against Kainic Acid Induced Excitotoxicity: An In Vitro

Stem cell therapy is gaining attention as a promising treatment option for neurodegenerative diseases. The functional efficacy of grafted cells is a matter of debate and the recent consensus is that the cellular and functional recoveries might be due to “by-stander” effects of grafted cells. In the present study, we investigated the neuroprotective effect of conditioned medium (CM) derived from human embryonic kidney (HEK) cells in a kainic acid (KA) induced hippocampal degeneration model system in in vitro condition. Hippocampal cell line was exposed to KA (200 µM) for 24 hrs (lesion group) whereas, in the treatment group, hippocampal cell line was exposed to KA in combination with HEK-CM (KA + HEK-CM). We observed that KA exposure to cells resulted in significant neuronal loss. Interestingly, HEK-CM cotreatment completely attenuated the excitotoxic effects of KA. In HEK-CM cotreatment group, the cell viability was ~85–95% as opposed to 47% in KA alone group. Further investigation demonstrated that treatment with HEK-CM stimulated the endogenous cell survival factors like brain derived neurotrophic factors (BDNF) and antiapoptotic factor Bcl-2, revealing the possible mechanism of neuroprotection. Our results suggest that HEK-CM protects hippocampal neurons against excitotoxicity by stimulating the host’s endogenous cell survival mechanisms

Privacy Preserving Data Mining for Ordinal Data using Correlation Based Transformation Strategy (CBTS)

Objectives: Preservation of privacy is a significant aspect of data mining. The main objective of PPDM is to hide or provide privacy to certain sensitive information so that they can be protected from unauthorized parties or intruders. Methods/Statistical Analysis: Though privacy is achieved by hiding the sensitive or private data, it will affect the data mining algorithms in knowledge extraction, so an effective method or strategy is required to provide privacy to the data and simultaneously protecting the quality of data mining algorithms. Instead of removing or encrypting sensitive or private data, we make use of data transformation strategies that keep the statistical, semantic and heuristic nature of data while protecting the sensitive or private data. Findings: In this paper we studied the technical feasibility of realizing Privacy Preserving Data Mining. In the proposed work, Correlation Based Transformation Strategy for Privacy Preserving Data Mining is used for ordinal data. We apply the method on few datasets namely soybean, Breast Cancer, Nursery dataset and Car dataset. We tabulate the end results applying the proposed strategy on both the original and the transformed dataset and observe correlation difference, Information Entropy and Classification Accuracy with different machine learning algorithms and Clustering Quality. Application/Improvements: As an improvement, the proposed work can be extended by use of vector marking techniques where these techniques help in increasing the efficiency by avoiding unauthorised access to the information