Histogramming privately ever after: Differentially-private data-dependent error bound optimisation

Abstract

© 2018 IEEE. Hay et al. (2016) recently observed that existing histogram release mechanisms under differential privacy do not provide satisfactory privacy protection. Existing work either tunes on sensitive data to optimise parameters without consideration of privacy; or selection is performed arbitrarily and independent of data, degrading utility. We address this open problem by deriving a principled tuning mechanism E2EPRIV that privately optimises data-dependent error bounds. Theoretical analysis establishes privacy and utility, while extensive experimentation demonstrates that E2EPRIV can practically achieve true end-To-end privacy.