End-to-End Differentially-Private Parameter Tuning in Spatial Histograms
This work solves the issue of rigorous privacy evaluation in location-based data analysis, which is crucial for applications like location privacy, though it is incremental as it builds on existing differential privacy mechanisms.
The paper tackled the problem of ensuring differential privacy in spatial histograms by addressing the missing component of private parameter tuning, which previous works often neglected, and demonstrated through experimentation that their method achieves true end-to-end privacy.
Differentially-private histograms have emerged as a key tool for location privacy. While past mechanisms have included theoretical & experimental analysis, it has recently been observed that much of the existing literature does not fully provide differential privacy. The missing component, private parameter tuning, is necessary for rigorous evaluation of these mechanisms. Instead works frequently tune on training data to optimise parameters without consideration of privacy; in other cases selection is performed arbitrarily and independent of data, degrading utility. We address this open problem by deriving a principled tuning mechanism that privately optimises data-dependent error bounds. Theoretical results establish privacy and utility while extensive experimentation demonstrates that we can practically achieve true end-to-end privacy.