PiMZ

PiMZ is an open‑source framework that was developed to facilitate the secure and efficient manipulation of large scientific datasets. The name stands for “Privacy‑preserving Machine‑Learning Zoo,” reflecting its dual focus on machine‑learning workflows and privacy‑by‑design principles. It was first introduced in 2019 by researchers at the University of Heidelberg and is distributed under a BSD‑3 license.

The original PiMZ release incorporated a modular architecture built on top of Python and Kubernetes, allowing users to deploy data pipelines that incorporate differential privacy and secure multiparty computation. Subsequent iterations added support for federated learning, automated model auditing, and a user‑friendly web interface. The project welcomes contributions from the wider academic community and maintains an active issue tracker on GitHub.

Technically, PiMZ exposes a set of core services: a data ingestion layer that synchronizes data from local and cloud sources, a privacy‑engine that applies configurable noise budgets, and a model‑deployment engine that wraps TensorFlow and PyTorch models in secure containers. The framework supports declarative pipeline definitions in YAML and provides diagnostic dashboards that expose privacy‑loss metrics in real time.

PiMZ has been adopted by several research labs studying sensitive biomedical data and by small enterprises developing privacy‑conscious AI products. Its modular design enables straightforward customization, making it suitable for both academic experimentation and production deployments. Future development plans include tighter integration with homomorphic encryption libraries, expanded support for edge devices, and the creation of a marketplace for pre‑validated privacy templates.