PETA: Privacy Enabled Task Allocation

Abstract

Service organizations need to comply with numerous data regulations to protect and preserve their customers’ privacy. Any misuse of data and privacy breach can affect the organizations’ reputation and brand image. In service delivery scenarios, such as IT support help desk, agents need to access customer data to serve them effectively. This data often includes sensitive and personally identifiable information of the customer. While some amount of data exposure is needed to serve a customer, however, exposure to more data than required could be a threat to an individual’s privacy. Hence, organizations need to design methodologies to ensure customer privacy while achieving minimal cost of operations.In this paper, we propose the Privacy Enabled Task Allocation (PETA) model for assigning customer requests to agents so that the overall cost of operations and data exposure is minimal. Data exposure is minimized by restricting the amount of data exposure per agent and by regulating the assignment of tasks. The PETA problem is modelled as an integer linear program, which is NP-hard. To solve this combinatorial hard problem, we have designed an allocation algorithm based on the linear programming relaxation for finding a quick feasible solution.