Package: heflow
HEflow: A Privacy-Preserving Machine Learning Lifecycle PlatformHEflow is a platform to streamline privacy-preserving machine learning
development, including tracking experiments, packaging code into reproducible
runs, and sharing and deploying encrypted models. Built on top ofMLflow,Seldon MLServerandOpenMined TenSEAL, HEflow offers a set
of lightweight homomorphic encryption APIs that can be used with any existing
machine learning application or library (scikit-learn, Keras, TensorFlow,
PyTorch, etc), wherever you currently run ML code (e.g. in notebooks, standalone
applications, or the cloud).Homomorphic Encryption (HE)Homomorphic encryption differs from typical encryption methods in that it allows
computation to be performed directly on encrypted data without requiring access
to a secret key. The result of such a computation remains in encrypted form, and
can at a later point be revealed by the owner of the secret key. This
ground-breaking technology has enabled industry and government to provide
never-before enabled capabilities for outsourced computation securely.Homomorphic encryption workflows, for privacy-preserving machine learning,
involve three entities:an ML model owner,a cloud server that performs model inference on HE encrypted data using the
pre-computed ML model, anda user who sends confidential data to the cloud for model inference.In all cases, the cloud should learn nothing about the underlying encrypted
data.Privacy-Preserving Machine Learning Operations (PPMLOps)PPMLOps is a set of processes and automated steps to manage code, data, and
encrypted models. This section describes a typical PPMLOps workflow.PPMLOps using HEflowExperimentsData scientists develop, train and tune the model on the production data,
then they encrypt ① and register it with the Encrypted Model Registry.
Model quality is evaluated by testing on held-out production data. This
pipeline can be triggered by code changes or by automated retraining jobs.Encrypted Model RegistryAutologging saves a record of the training and evaluation process, which
includes model metrics, parameters, tags, and the encrypted model itself.
When training and hyperparameter tuning are complete, the data scientist
registers the final encrypted model artifact in the Encrypted Model Registry
for the production environment. This records a link between the encrypted
model and the code used to generate it.Encrypted Model ServingA continuous deployment (CD) process takes new encrypted models and deploys
② them for low-latency online serving (APIs). Options include cloud
provider serving endpoints, or custom serving applications.InvocationsThe serving system loads the Production encrypted model version from the
Encrypted Model Registry. For each request, it scores the encrypted data
③, and returns encrypted predictions ④.What is HEflow?HEflow is an open source platform developed by InAccel to help manage the
complete privacy-preserving machine learning lifecycle with enterprise
reliability, security and scale. It tackles four primary functions::hammer_and_wrench: Encrypted Model developmentAccelerate and simplify privacy-preserving machine learning lifecycle management
with a standardized framework for developing production-ready PPML models. With
HEflow, you can bootstrap PPML projects, perform rapid iteration with ease and
ship high-quality encrypted models to production at scale.:clipboard: Experiment trackingRun experiments with any ML library, framework or language, and automatically
keep track of parameters, metrics, code and encrypted models from each
experiment. By using HEflow, you can securely share, manage and compare
experiment results along with corresponding artifacts and code versions.:jigsaw: Encrypted Model managementUse one central place to discover and share PPML models, collaborate on moving
them from experimentation to online testing and production, integrate with
approval and governance workflows and CI/CD pipelines, and monitor PPML
deployments and their performance. HEflow facilitates sharing of expertise and
knowledge, and helps you stay in control.:package: Encrypted Model deploymentQuickly deploy production encrypted models for batch inference or asgRPChomomorphic encryption APIs using built-in
integration with Docker containers orKServe. With HEflow, you can operationalize
and monitor production encrypted models to scale based on the business needs.InstallingInstall HEflow from PyPI viapip install heflowOfficial HEflow Docker ImageThe official HEflow Docker image is available on Docker Hub athttps://hub.docker.com/r/inaccel/heflow.# Pull the latest versiondockerpullinaccel/heflow