Coalition operations are essential for responding to the increasing number of world-wide incidents that require large-scale humanitarian assistance. Many nations and non-governmental organizations regularly coordinate to address such problems but their cooperation is often impeded by limits on what information they are able to share. In this paper, we consider the     use of an advanced cryptographic technique called secure multi-party computation to enable coalition members to achieve joint objectives while still meeting privacy requirements . Our particular focus is on a multi-nation aid delivery scheduling task that involves coordinating when and where various aid provider nations will deliver relief materials after the occurrence of a natural disaster . Even with the use of secure multi-party computation technology, information about private data can leak .  We describe how the emerging field of  quantitative information flow can be used to help data owners understand the extent to which private data might become vulnerable as the result of possible or actual scheduling operations, and to enable automated adjustments of the scheduling process to ensure privacy requirements Coalition operations are becoming an increasing focus for many nations. The need for collaboration derives from increasing awareness of mutual interests among both allies and nations that traditionally have not worked closely together. For example, coalition operations for Humanitarian Assistance and Disaster Relief (HADR) have increased substantially in recent years in both numbers and scope. With the impact of global warming, it is anticipated that there will be even more large-scale humanitarian crises resulting from adverse weather-related events and sea-level rises. These coalitions often involve not just government and military organizations but also non-governmental organizations (NGOs) and commercial entities.A key challenge facing coalitions is how to collaborate without releasing information that could jeopardize national (or organizational) interests. Information security mechanisms for the military to date have focused on safeguarding information by limiting its access and use. This approach has lead to a significant undersharing problem, which impedes Distribution Statement A: Approved for Public Release; Distribution is Unlimited.effective joint operations. Recent work on defining access control mechanisms is useful for enabling selective sharing of information that is safe to release BID4 . However, many coalition tasks require information that participants do not wish to make available to others.For example, consider the problem of scheduling the delivery of aid (food, water, medicine, fuel) to impacted nations after a natural disaster. Historically, international response has involved dozens of countries and NGOs, each with the desire to contribute in interconnected and potentially conflicting ways. Ideally coordination would be achieved by directly sharing information about the amount of aid each contributor has available or can produce, where that aid is situated, the position and storage capacity of ships for delivering the aid, harbor facilities where aid ships could dock, etc. But the owners of this data may be unwilling to directly share it with coalition partners, for fear of revealing information that impacts national security (e.g., ship locations) or competitive advantages (e.g., a company's backlog of inventory).To address this problem of coalition collaboration without revealing private information, we exploit a cryptographic technology called secure multi-party computation (MPC) BID6 . MPC protocols enable mutually distrusting parties to perform joint computations on private information while it remains encrypted. In our work, we use MPC to enable privacy-preserving computations over several types of coordination tasks related to scheduling aid delivery.While participants cannot directly learn others' private inputs from the process/protocol of a secure multi-party computation, they may be able to infer something about private data based on the results of the computation. For this reason, our privacy-aware scheduling solution makes use of a complementary technology called quantitative information flow (QIF) to measure the degree to which private data used in the scheduling task might leak to other participants. Insights gained from this analysis are folded back into the scheduling process via an adaptive workflow capability, to ensure that the vulnerability of private data stays within acceptable thresholds.The paper is organized as follows. We begin by summarizing our aid distribution task, including a description of the core scheduling problem and the data (private and non-private) belonging to the various coalition members. We then provide a short overview of secure multi-party computation followed by a description of how we employ it within a broader adaptive workflow framework to address the aid delivery scheduling task. Next, we describe our use of quantitative information flow to assess the vulnerability of private information as the scheduling progresses and to adapt the scheduling workflow in light of those assessments to ensure adherence to predefined privacy requirements. We conclude by identifying directions for future work and summarizing our contributions. A key challenge facing coalitions is how to collaborate without releasing information that could jeopardize national (or organizational) interests. In this paper, we consider this challenge for a realistic scheduling problem tied to aid delivery. Our work makes several contributions. First, we show how state-of-the-art secure multi-party computation can be used to safeguard private information with an overall distributed scheduling solution to the aid delivery problem. A second contribution relates to the use of quantitative information flow (QIF): even with secure multi-party computation, scheduling outputs can reveal information about coalition members' private data. We show how QIF can be applied to assess the vulnerability of providate data for both prospective (i.e., where results are not known) and actual (i.e., where results are known) computations. As a third contribution, these assessments can be used to adapt the scheduling algorithm to ensure it remains within accepted vulnerability thresholds established by data owners.
Privacy can be thought about in the same way as other resources in planning
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