K
Krishnamurty Muralidhar
Researcher at University of Oklahoma
Publications - 61
Citations - 2682
Krishnamurty Muralidhar is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Data masking & Differential privacy. The author has an hindex of 21, co-authored 55 publications receiving 2477 citations. Previous affiliations of Krishnamurty Muralidhar include Florida International University & University of Nebraska–Lincoln.
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An empirical investigation of the relationship between change in corporate social performance and financial performance: A stakeholder theory perspective
TL;DR: In this article, the authors investigated the relationship between corporate social performance (CSP) and corporate financial performance by examining how change in CSP is related to change in financial accounting measures.
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The Development of a Systematic, Aggregate Measure of Corporate Social Performance
TL;DR: This study proposes a methodology for the development of a systematic measure of CSP using the Analytic Hierarchy Process, a multi-criteria decision making technique that allows for the conversion of a multidimensional scale to a unidimensional Scale, enabling analysis/comparison of companies both within the same industry and across industries.
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A General Additive Data Perturbation Method for Database Security
TL;DR: This study describes a new method (General Additive Data Perturbation) that does not change relationships between attributes and when the database has a multivariate normal distribution, the new method provides maximum security and minimum bias.
Journal Article
Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data
TL;DR: The results indicate that Laplace noise addition delivers the promised level of privacy only by adding a large quantity of noise for even relatively large subsets, and raises serious questions regarding the viability of Laplace based noise addition for masking numeric data.
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Data ShufflingA New Masking Approach for Numerical Data
TL;DR: Data shuffling overcomes reservations about using perturbed or modified confidential data because it retains all the desirable properties of perturbation methods and performs better than other masking techniques in both data utility and disclosure risk.