Design of Policy-Aware Differentially Private Algorithms
This addresses the challenge of applying differential privacy in real-world settings with custom privacy policies, offering a method to improve error efficiency for specific query types.
The paper tackles the problem of designing error-optimal differentially private algorithms for variants with modified neighbor definitions, showing a transformational equivalence that reduces this to standard differential privacy for a large class of policies, and develops efficient algorithms for histograms and range queries.
The problem of designing error optimal differentially private algorithms is well studied. Recent work applying differential privacy to real world settings have used variants of differential privacy that appropriately modify the notion of neighboring databases. The problem of designing error optimal algorithms for such variants of differential privacy is open. In this paper, we show a novel transformational equivalence result that can turn the problem of query answering under differential privacy with a modified notion of neighbors to one of query answering under standard differential privacy, for a large class of neighbor definitions. We utilize the Blowfish privacy framework that generalizes differential privacy. Blowfish uses a {\em policy graph} to instantiate different notions of neighboring databases. We show that the error incurred when answering a workload $\mathbf{W}$ on a database $\mathbf{x}$ under a Blowfish policy graph $G$ is identical to the error required to answer a transformed workload $f_G(\mathbf{W})$ on database $g_G(\mathbf{x})$ under standard differential privacy, where $f_G$ and $g_G$ are linear transformations based on $G$. Using this result, we develop error efficient algorithms for releasing histograms and multidimensional range queries under different Blowfish policies. We believe the tools we develop will be useful for finding mechanisms to answer many other classes of queries with low error under other policy graphs.