3 papers
cs.LG2026
UnPII: Unlearning Personally Identifiable Information with Quantifiable Exposure Risk
Intae Jeon, Yujeong Kwon, Hyungjoon Koo
The ever-increasing adoption of Large Language Models in critical sectors like finance, healthcare, and government raises privacy concerns regarding the handling of sensitive Perso…
cs.CR2025
A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis
Omar Abusabha, Jiyong Uhm, Tamer Abuhmed +1
A function inlining optimization is a widely used transformation in modern compilers, which replaces a call site with the callee's body in need. While this transformation improves…
cs.SE2023
A Study of Rarely Appeared Instructions in an Executable Binary
Nozima Murodova, Hyungjoon Koo
An executable binary typically contains a large number of machine instructions. Although the statistics of popular instructions is well known, the distribution of non-popular instr…