@article{Avent_Korolova_Zeber_Hovden_Livshits_2019, title={BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model}, volume={9}, url={https://journalprivacyconfidentiality.org/index.php/jpc/article/view/680}, DOI={10.29012/jpc.680}, abstractNote={<pre style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;"><span style="color: #000000;">We propose a <em>hybrid</em></span><span style="color: #000000;"> model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of <em>blended </em></span><span style="color: #000000;">algorithm that improves the utility of obtained data, while providing users with their desired privacy guarantees.</span></pre> <pre style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;"><span style="color: #000000;">We apply this algorithm to the task of privately computing the head of the search log and show that the blended approach provides significant improvements in the utility of the data compared to related work.</span></pre> <pre style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;"><span style="color: #000000;">Specifically, on two large search click data sets, comprising </span><span style="color: #000000;">1.75 and </span><span style="color: #000000;">16 GB, respectively, our approach attains NDCG </span><span style="color: #000000;">values exceeding </span><span style="color: #000000;">95</span><span style="color: #000000;">% across a range of privacy budget values.</span></pre>}, number={2}, journal={Journal of Privacy and Confidentiality}, author={Avent, Brendan and Korolova, Aleksandra and Zeber, David and Hovden, Torgeir and Livshits, Benjamin}, year={2019}, month={Sep.} }