BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
This work addresses privacy concerns in search log analysis for users with varying privacy preferences, offering a novel blended algorithm that improves data utility while maintaining desired guarantees.
The paper tackled the problem of privately computing search log heads by proposing a hybrid differential privacy model combining local and trusted curator approaches, achieving NDCG values over 95% on large datasets up to 16 GB.
We propose a hybrid 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 blended algorithm for the task of privately computing the head of a search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets, comprising 1.75 and 16 GB respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.