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Location data are often aggregated to support a wide range of applications, such as mobility management, point-of-interest recommendation, map inferences, and so on. However, these aggregated results might suffer from poisoning attacks when adversaries deliberately send poisoned locations to the aggregator, namely, garbage in, garbage out. Most existing work focuses on poisoning attacks in anomaly detection, crowd sensing, recommendation, and machine learning models. One observation motivating our work is that poisoning attacks in the context of data aggregation introduce new challenges along with distinguished features compared to anomaly detection, recommendation systems and machine learning models. As such, in this paper, we concentrate on the inputs to data aggregation and introduce the first optimal attack framework that launches p oisoning a ttacks on l ocation d ata a ggregation …