Security-typed languages are an evolving tool for implementing systems with provable security guarantees. However, to date, these tools have only been used to build simple "toy" programs. As described in this paper, we have developed the first real-world, security-typed application: a secure email system written in the Java language variant Jif. Real-world policies are mapped onto the information flows controlled by the language primitives, and we consider the process and tractability of broadly enforcing security policy in commodity applications. We find that while the language provided the rudimentary tools to achieve low-level security goals, additional tools, services, and language extensions were necessary to formulate and enforce application policy. We detail the design and use of these tools. We also show how the strong guarantees of Jif in conjunction with our policy tools can be used to evaluate security. This work serves as a starting point-we have demonstrated that it is possible to implement real-world systems and policy using security-typed languages. However, further investigation of the developer tools and supporting policy infrastructure is necessary before they can fulfill their considerable promise of enabling more secure systems.
Abstract. We study the problem of maximizing the amount of stochastic diffusion in a network by acquiring nodes within a certain limited budget. We use a Sample Average Approximation (SAA) scheme to translate this stochastic problem into a simulation-based deterministic optimization problem, and present a detailed empirical study of three variants of the problem: where all purchases are made upfront, where the budget is split but one still commits to purchases from the outset, and where one has the ability to observe the stochastic outcome of the first stage in order to "re-plan" for the second stage. We apply this to a Red Cockaded Woodpecker conservation problem. Our results show interesting runtime distributions and objective value patterns, as well as a delicate trade-off between spending all budget upfront vs. saving part of it for later.
In network interdiction problems, evaders (e.g., hostile agents or data packets) are moving through a network toward targets and we wish to choose locations for sensors in order to intercept the evaders. The evaders might follow deterministic routes or Markov chains, or they may be reactive, i.e., able to change their routes in order to avoid the sensors. The challenge in such problems is to choose sensor locations economically, balancing interdiction gains with costs, including the inconvenience sensors inflict upon innocent travelers. We study the objectives of (1) maximizing the number of evaders captured when limited by a budget on sensing cost and, (2) capturing all evaders as cheaply as possible. We give algorithms for optimal sensor placement in several classes of special graphs and hardness and approximation results for general graphs, including evaders who are deterministic, Markov chain-based, reactive and unreactive. A similar-sounding but fundamentally different problem setting was posed by Glazer and Rubinstein where both evaders and innocent travelers are reactive. We again give optimal algorithms for special cases and hardness and approximation results on general graphs.
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