  We present a novel technique for modulating the appearance frequency of a few
tokens within a dataset for encoding an invisible watermark that can be used to
protect ownership rights upon data. We develop optimal as well as fast
heuristic algorithms for creating and verifying such watermarks. We also
demonstrate the robustness of our technique against various attacks and derive
analytical bounds for the false positive probability of erroneously detecting a
watermark on a dataset that does not carry it. Our technique is applicable to
both single dimensional and multidimensional datasets, is independent of token
type, allows for a fine control of the introduced distortion, and can be used
in a variety of use cases that involve buying and selling data in contemporary
data marketplaces.
