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Differential privacy (DP) enables private data analysis but is hard to use inpractice. For data controllers who decide what output to release, choosing theamount of noise to add to the output is a non-trivial task because of thedifficulty of interpreting the privacy parameter $\epsilon$. For data analystswho submit queries, it is hard to understand the impact of the noise introducedby DP on their tasks.  To address these two challenges: 1) we define a privacy risk indicator thatindicates the impact of choosing $\epsilon$ on individuals' privacy and usethat to design an algorithm that chooses $\epsilon$ automatically; 2) weintroduce a utility signaling protocol that helps analysts interpret the impactof DP on their downstream tasks. We implement the algorithm and the protocolinside a new platform built on top of a data escrow, which allows thecontroller to control the data flow and achieve trustworthiness whilemaintaining high performance. We demonstrate our contributions through anIRB-approved user study, extensive experimental evaluations, and comparisonwith other DP platforms. All in all, our work contributes to making DP easierto use by lowering adoption barriers.

Making Differential Privacy Easier to Use for Data Controllers and Data Analysts using a Privacy Risk Indicator and an Escrow-Based Platform