Inference management, trust and obfuscation principles for quality of information in emerging pervasive environments

Abstract

The emergence of large scale, distributed, sensor-enabled, machine-to-machine pervasive applications necessitates engaging with providers of information on demand to collect the information, of varying quality levels, to be used to infer about the state of the world and decide actions in response. In these highly fluid operational environments, involving information providers and consumers of various degrees of trust and intentions, information transformation, such as obfuscation, is used to manage the inferences that could be made to protect providers from misuses of the information they share, while still providing benefits to their information consumers. In this paper, we develop the initial principles for relating to inference management and the role that trust and obfuscation plays in it within the context of this emerging breed of applications. We start by extending the definitions of trust and obfuscation into this emerging application space. We, then, highlight their role as we move from the tightly-coupled to loosely-coupled sensory-inference systems and describe how quality, value and risk of information relate in collaborative and adversarial systems. Next, we discuss quality distortion illustrated through a human activity recognition sensory system. We then present a system architecture to support an inference firewall capability in a publish/subscribe system for sensory information and conclude with a discussion and closing remarks.