Contents 1 Privacy Preservation on Time Series 1

In this chapter, we discuss the problem of time series privacy preservation. A time series is a sequence of values that represent observations taken at constant time intervals. Time series data are prevalent in a wide range of domains and applications. However, data owners or publishers may not be willing to reveal the exact values of the time series due to privacy considerations. Thus, the data have to be modified in order to preserve privacy of individuals or organizations associated with the data. We consider privacy preservation using the perturbation method that introduces uncertainty to the data. In this method, the actual values of the time series are modified by adding noise. However, if the additional noise does not have the same compressibility properties as the original data, then it can be detected and filtered out, reducing uncertainty and therefore privacy. Thus, by making the perturbation “similar” to the original data, we can both preserve the structure of the data better and also make breaches harder. However, as data become more compressible, a fraction of overall uncertainty can be removed if true values are leaked, revealing how they were perturbed. Consequently, if we consider both leaks