DSAT Sample Clauses

DSAT. In DSFT, a prior knowledge on D is needed for the user to xx- xxxxxxx an appropriate value T . Suppose there exists an optimal value of T which can enable the algorithm to exactly generate C DP histograms. If the threshold T is higher than the optimal value, there will be remaining privacy budgets that are not utilized. On the contrary, if T is smaller than the optimal value, the privacy budget will be exhausted prematurely, resulting in update errors for remaining time points. In this section, we present DSAT, Distance-based Sampling with Adaptive Thresh- old, that releases a series of DP dynamic histograms while adaptively adjusting the threshold Ti for each time point, based on data dynamics. With DSAT, we do not have to find an optimal value of T, which may be difficult in practice. ~ H , H ~ d > T Figure 4.1. DSAT Framework Figure 4.1 illustrates the framework of DSAT. Intuitively, we wish to have C sampling points over N time points, hence our target sampling rate is C . Suppose 45.2 PRIVACY-PRESERVING DYNAMIC HISTOGRAM RELEASE WITH DISTANCE-BASED SAMPLING we have released Ci histograms at ti. If Ci < C , we need to decrease the threshold to allow more sampling time points, and vice versa. For each ti, we adjust the threshold based on the feedback error between the update ratio Ci at ti and the target ratio C , which is formally defined below. 4.2.1 (Feedback Error). We define the feedback error Ei at ti as follows:
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