The problem of frequent private information “leakage” from the myriad large centralized searchable data repositories in use today drives the need for an analytical framework that quantifies unequivocally how safe private data can be (privacy) while still providing measurable benefit (utility) to multiple legitimate information consumers. Rate distortion theory is shown to be a natural choice to develop such a framework which includes modeling of data sources, developing application independent utility and privacy metrics, quantifying utility-privacy tradeoffs irrespective of the type of data sources or the methods of providing privacy, and developing a side-information model for dealing with questions of external knowledge.
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