Time Series Compressibility and Privacy

In this paper we study the trade-offs between time series compressibility and partial information hiding and their fundamental implications on how we should introduce uncertainty about individual values by perturbing them. More specifically, if the perturbation does not have the same compressibility properties as the original data, then it can be detected and filtered out, reducing uncertainty. Thus, by making the perturbation "similar" to the original data, we can both preserve the structure of the data better, while simultaneously making breaches harder. However, as data become more compressible, a fraction of the uncertainty can be removed if true values are leaked, revealing how they were perturbed. We formalize these notions, study the above trade-offs on real data and develop practical schemes which strike a good balance and can also be extended for on-the-fly data hiding in a streaming environment.

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