Differential privacy introduces randomness into the data or into the queries made on the data, significantly reducing the chances of identifying personal information. This method is gaining traction as a powerful tool in the anonymization toolkit, given its mathematical underpinning that provides stronger guarantees of privacy. Its effectiveness, however, is heavily dependent on the proper selection of parameters, which can be challenging to optimize without sacrificing the utility of the data.
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