SocialGenPod: Privacy-Friendly Generative AI Social Web Applications
  with Decentralised Personal Data Stores
Vidminas Vizgirda Rui Zhao Naman Goel
We present SocialGenPod, a decentralised and privacy-friendly way of
deploying generative AI Web applications. Unlike centralised Web and data
architectures that keep user data tied to application and service providers, we
show how one can use Solid -- a decentralised Web specification -- to decouple
user data from generative AI applications. We demonstrate SocialGenPod using a
prototype that allows users to converse with different Large Language Models,
optionally leveraging Retrieval Augmented Generation to generate answers
grounded in private documents stored in any Solid Pod that the user is allowed
to access, directly or indirectly. SocialGenPod makes use of Solid access
control mechanisms to give users full control of determining who has access to
data stored in their Pods. SocialGenPod keeps all user data (chat history, app
configuration, personal documents, etc) securely in the user's personal Pod;
separate from specific model or application providers. Besides better privacy
controls, this approach also enables portability across different services and
applications. Finally, we discuss challenges, posed by the large compute
requirements of state-of-the-art models, that future research in this area
should address. Our prototype is open-source and available at:
https://github.com/Vidminas/socialgenpod/.