A useful capability is that of classifying some agent's behavior using data from a sequence, or trace, of sensor measurements. The sensor selection problem involves choosing a subset of available sensors to ensure that, when generated, observation traces will contain enough information to determine whether the agent's activities match some pattern. In generalizing prior work, this paper studies a formulation in which multiple behavioral itineraries may be supplied, with sensors selected to distinguish between behaviors. This allows one to pose fine-grained questions, e.g., to position the agent's activity on a spectrum. In addition, with multiple itineraries, one can also ask about choices of sensors where some behavior is always plausibly concealed by (or mistaken for) another. Using sensor ambiguity to limit the acquisition of knowledge is a strong privacy guarantee, a form of guarantee which some earlier work examined under formulations distinct from our inter-itinerary conflation approach. By concretely formulating privacy requirements for sensor selection, this paper connects both lines of work in a novel fashion: privacy-where there is a bound from above, and behavior verification-where sensors choices are bounded from below. We examine the worst-case computational complexity that results from both types of bounds, proving that upper bounds are more challenging under standard computational complexity assumptions. The problem is intractable in general, but we introduce an approach to solving this problem that can exploit interrelationships between constraints, and identify opportunities for optimizations. Case studies are presented to demonstrate the usefulness and scalability of our proposed solution, and to assess the impact of the optimizations.

翻译：一种有用的能力是，通过传感器测量序列（即迹）中的数据对某个智能体的行为进行分类。传感器选择问题涉及选择可用传感器的一个子集，以确保生成的观测迹包含足够的信息，从而判断智能体的活动是否匹配某种模式。本文在泛化先前工作的基础上，研究了一种制定形式，其中可以提供多个行为路线图，并选择传感器以区分这些行为。这使得能够提出细粒度问题，例如，将智能体活动定位在某个谱系上。此外，通过多个路线图，还可以探讨传感器选择问题，其中某些行为总是看似被其他行为所掩盖（或混淆）。利用传感器模糊性来限制知识的获取是一种强有力的隐私保障，这种保障形式在早期一些工作中以不同于我们的路线间混淆方法的制定方式进行了研究。本文通过具体制定传感器选择的隐私需求，以新颖的方式将这两个研究方向联系起来：隐私——其中存在上界，以及行为验证——其中传感器选择存在下界。我们研究了由这两种界产生的最坏情况计算复杂度，证明在标准计算复杂度假设下，上界更具挑战性。该问题在一般情况下是棘手的，但我们引入了一种求解方法，该方法能够利用约束之间的相互关系，并识别优化机会。我们通过案例研究展示了所提出解决方案的实用性和可扩展性，并评估了优化措施的影响。