Weighted Privacy Integrated Queries (wPINQ)

Abstract: We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes.
This paper details the data analysis platform wPINQ, which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations (e.g., counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.

Getting wPINQ

The wPINQ prototype is currently available for download. The distribution contains a functional implementation of the current iteration of the wPINQ language. This implementation is suitable for experimentation and prototyping, but is not intended as industrial strength privacy technology.

Sistem requirements

wPINQ is available both as C# source code and a Visual Studio project. The easiest way to explore wPINQ is through the latest version of Visual Studio, but the source also compiles and runs under other C# platforms.

Installation instruction

To install wPINQ, do the following:

Download wPINQ



Davide Proserpio, Sharon Goldberg and Frank McSherry

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