Data-driven concerns in private data release, Sept. 2012. Talk at Stevens Institute of Technology; AT&T Labs; UMass Amherst; Rutgers University-Newark; Bell Labs; NYU-Abu Dhabi.

Many users are clamouring for privacy techniques so that they can publish, share or sell their data safely. There are many aspects of data that then have to be managed: the data is often sparse, multi-dimensional, and structured. Each of these requires mechanisms that are aware of these features, so that they provide output which is useful and efficient to compute, in addition to providing a privacy guarantee. This talk outlines some recent efforts to generate such practical mechanisms.

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