Prescriptive analytics with differential privacy

Prescriptive analytics is a mechanism that provides the best set of actions to be taken to prevent undesirable outcomes for a given instance. However, this mechanism is prone to privacy breaches if an adversary with subsidiary data is allowed multiple query access to it. So, we propose a differential privacy mechanism in prescriptive analytics to preserve data privacy. Differential privacy can be achieved with the help of sensitivity of the given actions. Roughly speaking, sensitivity is the maximum change in the given set of actions with respect to the change in the given instances. However, a general analytical form for the sensitivity of the prescriptive analytics mechanism is difficult to derive. So, we formulate a nested constrained optimization to solve the problem. We use synthetic data in the experiments to validate the behavior of the differential privacy mechanism with respect to different privacy parameter settings. The experiments with two real-world datasets—Student Academic Performance and Reddit dataset, demonstrate the usefulness of our proposed method in education and social policy design. We also propose a new evaluation measure called the prescription success rate to further investigate the significance of our proposed method.