1. A method, implemented on a processor, for generating sanitized data, comprising: dividing, using the processor, a first training dataset comprised of a plurality of training data items into a plurality of data subsets each including at least one training data item of the plurality of training data items of the first training dataset; based on the plurality of data subsets, generating, using the processor, a plurality of distinct anomaly detection micro-models; receiving, using, the processor, at least one additional anomaly detection micro-model that was shared by a remote device, wherein at least one of the plurality of micro-models are updated based at least in part on the at least one additional anomaly detection micro-model; testing, using the processor, at least one data item of a plurality of data items of a second training dataset of training data items against each of the plurality of micro-models to produce a score for the at least one tested data item; and generating, using the processor, at least one output dataset by removing one of the plurality of training data items from the second training dataset based on the score for the at least one tested data item.