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Some of the future work perspectives include applying cryptography techniques to DP, designing efficient schemes for DP implementation in practical life or change in privacy budgets to see its effectiveness.
Finally, a fixed number of iterations allows us to allocate privacy budgets easily, e.g., evenly split across iterations as in this work.
In conclusion, Line 1, Line 2, Lines 3-8, and Line 9 use 0, 0, 0, and ε privacy budgets, respectively.
The AUC of our method remains stable under all privacy budgets and is significantly close to the public data baseline that uses the complete private data set as public data.
Figures 3 and 4 illustrate the AUCs of each method under various privacy budgets from 0.5 to 4, where "Public—#" means the public data baseline methods with various sizes of public data.
In Figure 2, we showed how the three methods perform (Using their corresponding "best" controllable factors) given different external factors, which include the numbers of private data sets (1, 3, 5, 10 and 20), the percentages of public data (0.5%, 1%, 2%, 5 %, 10) and the privacy budgets (0.5, 1, 1.5, 2, 5).
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Division of privacy budget.
a. Instead of equally dividing the privacy budget to each layer of decision tree [35], design an adjustable privacy budget assignment strategy.
Data provider Data provider sets several privacy parameters (e.g. privacy budget, etc).
The situation gets worse if the given privacy budget is small.
And yet again, design of an optimal privacy budgeting strategy is a great challenge [16].
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