Krish Muralidhar

Dr. Muralidhar’s main research interest is data privacy. His research is on developing techniques that allow organizations to securely analyze, share, and disseminate confidential data without compromising privacy. Dr. Muralidhar received a patent for Data shuffling - a procedure that allows organizations to securely release or share sensitive data. Dr. Muralidhar has published articles in such journals as Operations Research, Management Science, Information Systems Research, and ACM Transactions on Database Systems. He has also presented at many national and international academic conferences and other venues.

Degrees:

Ph.D., Texas A&M University
M.B.A., Sam Houston State University
B.Sc, University of Madras, India

Accomplishments/Awards:

Dr. Muralidhar received the inaugural Distinguished Doctoral Alumni Award from the Mays Business School at Texas A&M University in 2006. His other awards include the Teaching Incentive Program Award from the Florida State University System in 1994, the Excellence in Research Award from Florida International University in 1995, Best Inter-disciplinary paper award from the Decision Sciences Institute in 2002, and the Best paper award from the America’s Conference on Information Systems in 2005.

Select Publications:

Muralidhar, K. and J. Domingo-Ferrer, “Statistical Disclosure Limitation Techniques for Protecting 2020 Decennial US Census: Still a Viable Option,” Journal of Official Statistics (forthcoming).

Muralidhar, K. and J. Domingo-Ferrer, “Database Reconstruction is not so Easy and Different from Reidentification,” Journal of Official Statistics (forthcoming).

Sanchez, D., J. Domingo-Ferrer, and K. Muralidhar, “Confidence-ranked Reconstruction of Census Records from Aggregate Statistics Fails to Capture Privacy Risks and Re-identifiability,” Proceedings of the National Academy of Sciences, 20(18), 2023.

Muralidhar, K., “A Re-examination of the Census Bureau Reconstruction and Reidentification Attack,” In: Domingo-Ferrer, J., Laurent, M. (eds) Privacy in Statistical Databases. PSD 2022. Lecture Notes in Computer Science, vol 13463. Springer, Cham.

Blanco-Justicia, A., D. Sanchez, D., J. Domingo-Ferrer, and K. Muralidhar, “A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning,” ACM Computing Surveys, 55(8), 1-16, 2023.

Domingo-Ferrer, J., K. Muralidhar, and M. Bras-Amoros, “General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model,” IEEE Transactions on Secure and Dependable Computing (in print).

Lin, Y-X, L. Mazur, R. Sarathy, and K. Muralidhar, “Statistical Information Recovery from Multivariate Noise-Multiplied Data: A Computational Approach” Transactions on Data Privacy 11(1), 23-45, 2018.

Palk, L. and Muralidhar, K., “Free Ride: Data Brokers’ Rent-Seeking Behavior and the Future of Data Inequality,” Vanderbilt Journal of Entertainment and Technology Law (Accepted for publication).

Muralidhar, K., “Record Re-Identification of Swapped Numerical Microdata,” Journal of Information Privacy and Security, 30(1), 34-45, 2017.

Domingo-Ferrer, J. and K. Muralidhar, “New Directions in Anonymization: Permutation Paradigm, Verifiability by Subjects and Intruders, Transparency to Users,” Information Sciences, 337–338, 11–24, 2016.

Muralidhar, K., H. Li, and R. Sarathy, “Secure Attribute Sharing of Linked Microdata,” Decision Support Systems, 81, 20–29, 2016.

Muralidhar, K., C. O’Keefe, and R. Sarathy, “A Bootstrap Mechanism for Response Masking in Remote Analysis Systems,” Decision Sciences 46(6), 1199-1226, 2015.

Bambauer, J.R., K. Muralidhar, and R. Sarathy, "Fool's Gold! An Illustrated Critique of Differential Privacy", Vanderbilt Journal of Entertainment & Technology Law, 16(4), 701-755, 2014.

Li, H., K. Muralidhar, R. Sarathy, and X. Luo, “Evaluating Re-identification Risks of Data Protected by Additive Data Perturbation,” Journal of Database Management 25(2), 2014.

Muralidhar, K. and R. Sarathy, “Interval Responses for Queries on Confidential Attributes: A Security Evaluation.” Journal of Information Privacy and Security, 9(1), 3-16, 2013.

Muralidhar, K. and R. Sarathy, "Statistical Dependence as the Basis for a Privacy Measure for Microdata Release," International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems, 20(6), 893-906, 2012.

Li, H., K. Muralidhar, and R. Sarathy, “The Effectiveness of Data Shuffling for Privacy-Preserving Data Mining Applications,” Journal of Information Privacy and Security, 8(2), 3-17, 2012

Trottini, M., K. Muralidhar, R. Sarathy, “An Investigation of Model Based Microdata Methods for Magnitude Tabular Data,” In Josep Domingo-Ferrer and Ilenia Tinnirello (Editors), Lecture Notes in Computer Science: Privacy in Statistical Databases 2012, pp. 47-62, Berlin: Springer-Verlag, 2012.

Sarathy, R. and K. Muralidhar, "Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data," Transactions on Data Privacy, 4(1), pp. 1-17, 2011.

Trottini, M., K. Muralidhar, and R. Sarathy, " Maintaining Tail Dependence in Data Shuffling using t Copula," Statistics and Probability Letters, 81 (2011), pp. 420-428.

Muralidhar, K. and R. Sarathy, " Generating Sufficiency-based Non-Synthetic Perturbed Data," Transactions on Data Privacy, 1(1), 17-33, 2008. (An implementation of the procedure described in this manuscript is available on μ-argus: http://neon.vb.cbs.nl/casc/mu.htm).

Li, H., K. Muralidhar, and R. Sarathy, “Assessment of Disclosure Risk when using Confidentiality via Camouflage,” Operations Research, 55(6), 1178-1182, 2007.

Muralidhar, K. and R. Sarathy, “A Comparison of Multiple Imputation and Data Perturbation for Masking Numerical Variables,” Journal of Official Statistics, 22(3), 507-524, 2006.

Muralidhar, K. and R. Sarathy, "Data Shuffling- A New Masking Approach for Numerical Data," Management Science, 52(5), 658-670, 2006.

Muralidhar, K. and R. Sarathy, "An Enhanced Data Perturbation Approach for Small Data Sets," Decision Sciences, 36(3), 513-529, 2005.

Muralidhar, K. and R. Sarathy, "A Rejoinder to the Comments by Polettini and Stander on 'A Theoretical Basis for Perturbation Methods'," Statistics and Computing, 13(4), 339-342, 2003.

Muralidhar, K. and R. Sarathy, "A Theoretical Basis for Perturbation Methods," Statistics and Computing, 13(4), 329-335, 2003.

Sarathy, R., K. Muralidhar, and R. Parsa, "Perturbing Non-Normal Confidential Attributes: The Copula Approach," Management Science, 48(12), 1613-1627, 2002.

Sarathy, R. and K. Muralidhar, "The Security of Confidential Numerical Data in Databases," Information Systems Research, 13(4), 389-403, 2002.

Muralidhar, K., R. Sarathy, and R. Parsa, "An Improved Security Requirement for Data Perturbation with Implications for E-Commerce," Decision Sciences, 32(4), 683-698, 2001.

Muralidhar, K. and R. Sarathy, "Security of Random Data Perturbation Methods," ACM Transactions on Database Systems, 24(4), 487-493, 1999.

Muralidhar, K., R. Parsa, and R. Sarathy, "A General Additive Data Perturbation Method for Database Security," Management Science, 45(10), 1399-1415, 1999.

Muralidhar, K., D. Batra, and P. Kirs, “Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach,” Management Science, 41(9), 1549-1564,1995.