3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CR2026
The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning
Sae Furukawa, Alina Oprea
Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks. Crowdsourcing user conversations is an established approach to collecting SFT data a…
cs.CR2026
Reconstruction of Personally Identifiable Information from Proprietary Data in Supervised Fine-Tuned Models
Sae Furukawa, Alina Oprea
Supervised Finetuning (SFT) has become one of the primary methods for adapting a large language model (LLM) with extensive pre-trained knowledge to domain-specific, instruction-fol…
cs.SD2024★ 3 cited
Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms
Jonat John Mathew, Rakin Ahsan, Sae Furukawa +6
Deepfake audio poses a rising threat in communication platforms, necessitating real-time detection for audio stream integrity. Unlike traditional non-real-time approaches, this stu…