Adding Laplace or Gaussian Noise to Database for Privacy

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Adding Laplace or Gaussian Noise to Database for Privacy

The idea of differential privacy is to guarantee bounds on how much information may be revealed by someone's participation in a database. These bounds are described by two numbers: ε (epsilon) and δ (delta). We're primarily interested in the multiplicative bound described by ε. This number is roughly the number of bits of information an analyst might gain regarding an individual.

The multiplicative bound is exp(ε) and so ε, the natural log of the multiplicative bound, would be the information measure, though technically in nats rather than bits since we're using natural logs rather than logs base 2.

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