Assistant Professor Department of Mathematics
Ghatak, D. and Roy, B.K., Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise, Journal of Official Statistics, Vol. 34, No. 3, 2018, pp. 1-24, DOI: http://dx.doi.org/10.2478/JOS2018-0032.
Ghatak, D. and Roy, B.K., Conditional Masking to Numerical Data, Journal of Statistical Theory and Practise, 2019, DOI 10.1007/s42519-019-0042-y
Ghatak, D. and Roy, B.K., An Improved Bound for Security in an Identity Disclosure Problem. International Journal of Statistics and Probability Vol 8, No. 3, 2019, pp. 24-31
Ghatak, D. and Sakurai, K. and Roy, B.K., Can Data Obfuscation techniques be beneficial for preserving Data Utility unlike Differentially Private Algorithms? IWSEC 2020.
Ghatak D. and Sakurai K., On the power and limitation of Laplace noise addition for private synthetic data generation. IWSEC 2022.
Ghatak D., Sengupta D and Roy B. Optimal Gamma density to Obfuscate Quantitative data with Added Noise, available at https://arxiv.org/abs/2208.07561
Ghatak D., Sakurai K. A Survey on Privacy Preserving Synthetic Data Generation and a Discussion on a Privacy-Utility Trade-off Problem, presented at The 4th International Conference on Science of Cyber Security 2022
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