Secure computation is becoming a key feature of future information systems. Distributed network applications and cloud architectures are at danger because lots of personal consumer data is aggregated in all kinds of formats and for various purposes. Industry and consumer electronics companies are facing massive threats like theft of intellectual property and industrial espionage. Public infrastructure has to be secured against sabotage and manipulation. A possible solution is encrypted computing: Data can be processed on remote, possibly insecure resources, while program code and data is encrypted all the time. This allows to outsource the computation of confidential information independently from the trustworthiness or the security level of the remote system. The technologies and techniques discussed in this workshop are a key to extend the range of applications that can be securely outsourced.

This article develops a utility-based optimization framework for resource sharing by multiple competing missions in a mission-oriented wireless sensor network (WSN) environment. Prior work on network utility maximization (NUM) based optimization has focused on unicast flows with sender-based utilities in either wireline or wireless networks. In this work, we develop a generalized NUM model to consider three key new features observed in mission-centric WSN environments: i) the definition of the utility of an individual mission (receiver) as a joint function of data from multiple sensor sources; ii) the consumption of each sender's (sensor) data by multiple missions; and iii) the multicast-tree-based dissemination of each sensor's data flow, using link-layer broadcasts to exploit the “wireless broadcast advantage” in data forwarding. We show how a price-based, distributed protocol (WSN-NUM) can ensure optimal and proportionally fair rate allocation across multiple missions, without requiring any coordination among missions or sensors. We also discuss techniques to improve the speed of convergence of the protocol, which is essential in an environment as dynamic as the WSN. Further, we analyze the impact of various network and protocol parameters on the bandwidth utilization of the network, using a discrete-event simulation of a stationary wireless network. Finally, we corroborate our simulation-based performance results of the WSN-NUM protocol with an implementation of an 802.11b network.