Publication Type

Conference Proceeding Article

Publication Date

8-2010

Abstract

In both the commercial and defense sectors a compelling need is emerging for rapid, yet secure, dissemination of information. In this paper we address the threat of information leakage that often accompanies such information flows. We focus on domains with one information source (sender) and many information sinks (recipients) where: (i) sharing is mutually beneficial for the sender and the recipients, (ii) leaking a shared information is beneficial to the recipients but undesirable to the sender, and (iii) information sharing decisions of the sender are determined using imperfect monitoring of the (un)intended information leakage by the recipients.We make two key contributions in this context: First, we formulate data leakage prevention problems as Partially Observable Markov Decision Processes; we show how to encode one sample monitoring mechanism—digital watermarking—into our model. Second, we derive optimal information sharing strategies for the sender and optimal information leakage strategies for a rational-malicious recipient as a function of the efficacy of the monitoring mechanism. We believe that our approach offers a first of a kind solution for addressing complex information sharing problems under uncertainty.

Keywords

data leakage prevention, digital watermarking, partially observable Markov decision processes

Discipline

Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT)

Publisher

IEEE

City or Country

Minneapolis

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.