LLM Personas Generate Executable Smart-Home Schedules to Replace Privacy-Risky Field Data

arXiv·medium signal

Usable-security and privacy research ideally draws on datasets from real homes, but collecting them is slow, expensive, and intrusive. This work uses LLMs to generate diverse resident personas that produce executable smart-home schedules — synthesizing authentic-looking device interactions and daily routines without long-term surveillance of real people. A practical example of LLM-generated synthetic data standing in for sensitive human behavioral datasets.