Sufficient conditions using the Wasserstein metric of order 1 are derived to calibrate Laplace noise for pufferfish privacy in multi-user aggregated queries, with relaxations for binary data that reduce noise while preserving indistinguishability.
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The authors provide a systematization of differentially private graph release methods along with an objective-based framework and two illustrative evaluations for social network analysts.
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Multi-user Pufferfish Privacy
Sufficient conditions using the Wasserstein metric of order 1 are derived to calibrate Laplace noise for pufferfish privacy in multi-user aggregated queries, with relaxations for binary data that reduce noise while preserving indistinguishability.
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SoK: Practical Aspects of Releasing Differentially Private Graphs
The authors provide a systematization of differentially private graph release methods along with an objective-based framework and two illustrative evaluations for social network analysts.