Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs. A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics. We propose %DIFDELCMD < {\em %%% private weighted sampling (PWS): A method that ensures element-level differential privacy while retaining, to the extent possible, the utility of a respective non-private weighted sample. PWS maximizes the reporting probabilities of keys and  improves over the state of the art also for the well-studied special case of %DIFDELCMD < {\em %%% private histograms , when no sampling is performed. We empirically demonstrate significant performance gains compared with prior baselines: 20 %DIFDELCMD < \\%%% %DIF < -300\\% increase in key reporting for common Zipfian frequency distributions and accuracy for \times 2- 8 lower frequencies in estimation tasks. Moreover, PWS is applied as a simple post-processing of a non-private sample, without requiring the original data. This allows for seamless integration with existing implementations of non-private schemes and retaining the efficiency of schemes designed for resource-constrained settings such as massive distributed or streamed data. We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.abstract 
Common datasets have the form of %DIFDELCMD < {\em %%% %DIFDELCMD < {\em %%% elements with keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% %DIFDELCMD < {\em %%% key and frequency pairs. A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics. We propose %DIFDELCMD < {\em %%% private weighted sampling (PWS): A method that ensures element-level differential privacy while retaining, to the extent possible, the utility of a respective non-private weighted sample. PWS maximizes the reporting probabilities of keys and estimation quality of a broad family of statistics. PWS improves over the state of the art also for the well-studied special case of %DIFDELCMD < {\em %%% private histograms , when no sampling is performed. We empirically demonstrate significant performance gains compared with prior baselines: 20 %DIFDELCMD < \\%%% %DIF < -300\\% increase in key reporting for common Zipfian frequency distributions and accuracy for \times 2- 8 lower frequencies in estimation tasks. Moreover, PWS is applied as a simple post-processing of a non-private sample, without requiring the original data. This allows for seamless integration with existing implementations of non-private schemes and retaining the efficiency of schemes designed for resource-constrained settings such as massive distributed or streamed data. We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.abstract \%-300\% increase in key reporting for common Zipfian frequency distributions and accuracy for \times 2- 8 lower frequencies in estimation tasks. Moreover, PWS is applied as a simple post-processing of a non-private sample, without requiring the original data. This allows for seamless integration with existing implementations of non-private schemes and retaining the efficiency of schemes designed for resource-constrained settings such as massive distributed or streamed data. We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.
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elements
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with
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keys
elements with keys
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key
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and
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frequency
key and frequency
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estimation quality of a broad family of statistics. PWS
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private histograms
private histograms
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\%-300\% increase in key reporting for common Zipfian frequency distributions and accuracy for \times 2- 8 lower frequencies in estimation tasks. Moreover, PWS is applied as a simple post-processing of a non-private sample, without requiring the original data. This allows for seamless integration with existing implementations of non-private schemes and retaining the efficiency of schemes designed for resource-constrained settings such as massive distributed or streamed data. We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.