3 papers
cs.CR2026
Challenges in Enabling Private Data Valuation
Yiwei Fu, Tianhao Wang, Varun Chandrasekaran
Data valuation methods quantify how individual training examples contribute to a model's behavior, and are increasingly used for dataset curation, auditing, and emerging data marke…
cs.SE2025
SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification
Tianyang Zhou, Ziyi Zhang, Haowen Lin +4
Translating software written in C to Rust has significant benefits in improving memory safety. However, manual translation is cumbersome, error-prone, and often produces unidiomati…
cs.LG2025
Verifiable and Provably Secure Machine Unlearning
Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran +3
Machine unlearning aims to remove points from the training dataset of a machine learning model after training: e.g., when a user requests their data to be deleted. While many unlea…