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Probabilistic Inference and Differential Privacy
Oliver Williams · Frank McSherry

Tue Dec 07 12:05 PM -- 12:10 PM (PST) @ Regency Ballroom

We identify and investigate a strong connection between probabilistic
inference and differential privacy, the latter being a recent privacy
definition that permits only indirect observation of data through
noisy measurement. Previous research on differential privacy has
focused on designing measurement processes whose output is likely to
be useful on its own. We consider the potential of applying
probabilistic inference to the measurements and measurement process to
derive posterior distributions over the data sets and model parameters
thereof. We find that probabilistic inference can improve accuracy,
integrate multiple observations, measure uncertainty, and even provide
posterior distributions over quantities that were not directly
measured.

Author Information

Oliver Williams (Microsoft Research)
Frank McSherry (Microsoft Research)

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