Rights Protection of Multidimensional Time-Series Datasets with Neighborhood Preservation

Industry companies frequently outsource datasets to mining firms and academic institutions create repositories and share datasets in the interest of promoting research collaboration. Still, many practitioners feel reserved about about sharing or outsourcing datasets, primarily because of the fear of losing the principal rights over the dataset. This work presents a way of convincingly claiming ownership rights over a trajectory dataset, without, at the same time, destroying the salient dataset characteristics, which are important for accurate search operations and data-mining tasks. The methodology that we present distorts imperceptibly a collection of sequences, effectively embedding a secret key, while retaining as well as possible the neighborhood of each object, which is vital for operations such as similarity search, classification or clustering.