Add conference colab #248
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Add conference colab #248
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miracvbasaran
approved these changes
Feb 25, 2022
| "id": "e2SOjo8qiNnw" | ||
| }, | ||
| "source": [ | ||
| "The goal of this Colab is to demonstrate how to compute the count of participants aggregated by country in a DP manner.\n", |
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nit: This is the first time we are mentioning DP, so it might make sense to not use and abbreviations, i.e. "in a differentially private (DP) manner."
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "This is a simple example that shows how to calculate anonymized statistics using PipelineDP. The input data is a simulated dataset of an imaginary conference participants including their origin coutries. We use PipelineDP to calculate anonymized count of participants aggregated by country." |
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nit: coutries -> countries
| "# metrics to compute.\n", | ||
| "dp_result = dp_engine.aggregate(input, params, data_extractors)\n", | ||
| "\n", | ||
| "# Compute budget per each DP operation. \n", |
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nit: delete period at the end for consistency (or alternatively, put periods at the end of other comments.
| "fig, ax = plt.subplots()\n", | ||
| "rects1 = ax.bar(x - width/2, non_dp_count, width, label='non-DP')\n", | ||
| "rects2 = ax.bar(x + width/2, dp_count, width, label='DP')\n", | ||
| "ax.set_title('Count participants per country')\n", |
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nit: Count of participants per country (also on the non-DP graph above)
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The colab includes the example that we often use in presentations. We count participants aggregated by the country of origin.