Some Remarks and Ideas About Monetization of Sensitive Data

Piotrowska, Ania M.; Klonowski, Marek

doi:10.1007/978-3-319-29883-2_8

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Moreover, as pointed in some recent papers, adding noise may lead to significant errors in aggregated statistic. Even if having noisy response is acceptable for a given scenario, the resulting statistics may be too far from the exact values to be usable in practice (see [20,30]). Finally, adding noise, specifically from a non-standard distribution, can be technically problematic -especially when the aggregated data may come from small, computationally constrained devices.…”

Section: Introductionmentioning

confidence: 99%

Towards Extending Noiseless Privacy -- Dependent Data and More Practical Approach

Grining,

Klonowski

2016

Preprint

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In 2011 Bhaskar et al. pointed out that in many cases one can ensure sufficient level of privacy without adding noise by utilizing adversarial uncertainty. Informally speaking, this observation comes from the fact that if at least a part of the data is randomized from the adversary's point of view, it can be effectively used for hiding other values.So far the approach to that idea in the literature was mostly purely asymptotic, which greatly limited its adaptation in real-life scenarios. In this paper we aim to make the concept of utilizing adversarial uncertainty not only an interesting theoretical idea, but rather a practically useful technique, complementary to differential privacy, which is the state-of-the-art definition of privacy. This requires non-asymptotic privacy guarantees, more realistic approach to the randomness inherently present in the data and to the adversary's knowledge.In our paper we extend the concept proposed by Bhaskar et al. and present some results for wider class of data. In particular we cover the data sets that are dependent. We also introduce rigorous adversarial model. Moreover, in contrast to most of previous papers in this field, we give detailed (non-asymptotic) results which is motivated by practical reasons. Note that it required a modified approach and more subtle mathematical tools, including Stein method which, to the best of our knowledge, was not used in privacy research before.Apart from that, we show how to combine adversarial uncertainty with differential privacy approach and explore synergy between them to enhance the privacy parameters already present in the data itself by adding small amount of noise.

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Section: Introductionmentioning

confidence: 99%