Communications of the ACM
The Limits of Differential Privacy (and Its Misuse in Data Release and Machine Learning)
Credit: Andrij Borys Associates, Shutterstock
The traditional approach to statistical disclosure control (SDC) for privacy protection is utility-first. Since the 1970s, national statistical institutes have been using anonymization methods with heuristic parameter choice and suitable utility preservation properties to protect data before release. Their goal is to publish analytically useful data that cannot be linked to specific respondents or leak confidential information on them.
In the late 1990s, the computer science community took another angle and proposed privacy-first data protection. In this approach a privacy model specifying an ex ante privacy condition is enforced using one or several SDC methods, such as noise addition, generalization, or microaggregation. The parameters of the SDC methods depend on the privacy model parameters, and too strict a choice of the latter may result in poor utility. The first widely accepted privacy model was k-anonymity, whereas differential privacy (DP) is the model that currently attracts the most attention.
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