Differential

I already explained DP ML in another post 1, so this blog post covers the question, how can we design a service that lets customers finetune Large Language Models in a privacy preserving way. With the rise of data privacy laws like GDPR, DSGVO and CCPA, companies face increased scrutiny on data handling practices. The demand for privacy-preserving AI models is growing, especially in highly regulated industries. Despite this demand, many businesses lack the in-house expertise to implement their own model fine-tuning....

In this post, I want to briefly introduce Differential Privacy to you, which, in my honest opinion, needs to get more attention in the software developer community. During my Master thesis, I evaluated the use of Differential Privacy for Federated Learning (I might explain Federated Learning in another post). The Theory Differential Privacy, originally $\epsilon$-Differential Privacy (DP)1, is a way to secure the privacy of individuals in a statistical database. A statistical database is a database, where only aggregation functions like “sum”, “average”, “count”, et cetera… can be executed....