Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models
This addresses privacy issues for smart home users, though it is incremental as it builds on existing LLM and privacy-preserving techniques.
The paper tackles the privacy concerns of transmitting user data to remote servers in LLM-based smart home assistants by developing HomeLLaMA, an on-device assistant with a tailored small language model, which enhances user privacy while providing personalized services, as demonstrated in experiments with 100 users.
Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about the potential privacy leaks to the remote servers. To address this issue, we develop HomeLLaMA, an on-device assistant for privacy-preserving and personalized smart home serving with a tailored small language model (SLM). HomeLLaMA learns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed, HomeLLaMA facilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user experience while protecting their privacy, we develop PrivShield to offer an optional privacy-preserving LLM-based smart home serving for those users, who are unsatisfied with local responses and willing to send less-sensitive queries to remote servers. For evaluation, we build a comprehensive benchmark DevFinder to assess the service quality. Extensive experiments and user studies (M=100) demonstrate that HomeLLaMA can provide personalized services while significantly enhancing user privacy.