Asha Kiran

Bio: Asha Kiran is an academic researcher. The author has contributed to research in topics: Homomorphic encryption & Fuzzy logic. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Preserving Sensitive Information using Fuzzy C-Means Approach

Abstract: Privacy is one of the important issues now days as privacy is linked with multidimensional issues; security, sentiment, fear, emotions, threats etc. Protecting privacy is as much as data utilization. In this day and age, data is getting generated largely by various industries. Medical industry is one of them. Providing safe access controls and privacy preservation are the primary concerns in the development of medical applications. Medical data possess sensitive information. According to the author, privacy should be preserved at all levels; storage level, to view level to knowledge discovery level. At view level, very limited approaches are proposed to protect the privacy of the medical data. This paper implements Fuzzy C means approach to protect the sensitive data while viewing blood donor data online. In this paper, a sample blood donor records are extracted to categorize the data into high sensitive data and low sensitive data using fuzzy C means rules. Subsequently, the model teaches the underlying relations to perform categorization based on the input. This paper describes the experiment in view of privacy preserving data mining. The experiment is simulated using MATLAB and shows satisfactory result.

Privacy Preserving Model using Homomorphic Encryption

Abstract: Privacy preserving is utmost important in medical applications. Cryptography has numerous techniques to safe guard the privacy of the data. It is practice to use private key for encryption and public key for decryption in the area of cryptography. Conventionally, without decryption, data usability is difficult. However, the complications outweigh the private and public keys. This paper presents privacy preserving model based on Homomorphic encryption technique and model evaluation using classification technique. The homomorphic model highlights usability of the data without decryption. The objective of this paper is to show how the encrypted data is preserving underlying relations through classification tree. This paper presents two parts: Part-I describes the model building on medical data using PSO optimization and filer based co-efficient matrix (for encryption) to protect privacy of the data and part-II describes model evaluation using classification tree and clustering technique. The performance of the encryption is tested using predictive modelling technique (classification tree technique) and K-Means clustering technique, to assess whether the underlying relations are preserved in the encrypted data. The experimental results show that the underlying classification accuracy of encrypted data and source data (non-encrypted) is just varying by +/5%.

A Robust Approach to Secure Structured Sensitive Data using Non-Deterministic Random Replacement Algorithm

Abstract: The first list of Jan 2018 is one of the longest lists, with a count of 7,073,069cases, which include Cyber attacks & ransom ware, Data breaches, financial information, and others.Security and risk management leaders should use data masking to desensitize or protect sensitive data and address the changing threat and compliance landscape. Masking is a philosophy or new way of thinking about safeguarding sensitive data in such a way that accessible and usable data is still available for nonproduction environment. In this research paper authors proposed a dynamic data masking model to protect sensitive data using non-deterministic randomreplacement algorithm. This paper contains comparative analysis of proposed model with existing masking methods and result shows that proposed model is would be superior in terms of sensitive data discovery, dynamic data masking and data security.