14 citations · 34 across the 11 of their papers we have counts for
4 papers · 1 filter
L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response
Han Wang, Hanbin Hong, Li Xiong +2
Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy conc…
Certified Adversarial Robustness via Anisotropic Randomized Smoothing
Hanbin Hong, Yuan Hong
Randomized smoothing has achieved great success for certified robustness against adversarial perturbations. Given any arbitrary classifier, randomized smoothing can guarantee the c…
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing
Hanbin Hong, Binghui Wang, Yuan Hong
We study certified robustness of machine learning classifiers against adversarial perturbations. In particular, we propose the first universally approximated certified robustness (…
An Eye for an Eye: Defending against Gradient-based Attacks with Gradients
Hanbin Hong, Yuan Hong, Yu Kong
Deep learning models have been shown to be vulnerable to adversarial attacks. In particular, gradient-based attacks have demonstrated high success rates recently. The gradient meas…