Differentially Private Group-by Data Releasing Algorithm

Abstract

Privacy concerns are growing fast because of data protection regulations around the world. Many works have built private algorithms avoiding sensitive information leakage through data publication. Differential privacy, based on formal definitions, is a strong guarantee for individual privacy and the cutting edge for designing private algorithms. This work proposes a differentially private group-by algorithm for data publication under the exponential mechanism. Our method publishes data groups according to a specified attribute while maintaining the desired privacy level and trustworthy utility results.

Optical absorption of single-layer hexagonal boron nitride in the ultraviolet

“…We note that, incidentally, this value of E g coincides with that of [35,39] (see table 3). b A self-consistent calculation using no approximations to the screened potential and with r 0 determined via a RPA calculation, gives r 0 = 7.2 Å and E scr g ≈ 7.2 eV [41].…”