Publication Type : Conference Paper
Thematic Areas : Learning-Technologies
Publisher : 2018 IEEE International Conference on Data Mining (ICDM) .
Source : 2018 IEEE International Conference on Data Mining (ICDM) (2018)
Url : https://www.scopus.com/record/display.uri?eid=2-s2.0-85061364246&doi=10.1109%2fICDM.2018.00124&origin=inward&txGid=5333c7ff3f0771ad9a6dfbd8a7a5e940
Campus : Coimbatore
School : School of Engineering
Center : Technologies & Education (AmritaCREATE), Amrita Center For Research in Analytics
Department : Sciences
Year : 2018
Abstract : Privacy preservation is important. Prescriptive analytics is a method to extract corrective actions to avoid undesirable outcomes. We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. We use differential privacy mechanism to achieve strong privacy guarantee. Differential privacy mechanism requires computation of sensitivity: maximum change in the output between two training datasets, which is differed by only one instance. The main challenge we addressed is the computation of sensitivity of the prescription vector. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity. We solve the optimization problem using constrained Bayesian optimization, as the nested structure makes the objective function expensive. We demonstrate our algorithm on two real world datasets and observe that the prescription vectors remains useful even after making them private. © 2018 IEEE.
Cite this Research Publication : H. Harikumar, Rana, S., Gupta, S., Nguyen, T., Kaimal, R., and Venkatesh, S., “Differentially Private Prescriptive Analytics”, in 2018 IEEE International Conference on Data Mining (ICDM), 2018.