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Roadmap#

Cape Privacy's software is currently in alpha state. This document describes Cape Privacy's goals, and some upcoming features.

The goal#

Cape Privacy aims to enable better and more privacy compliant data science, and to make this accessible to a wide range of users (not just engineers).

Through our open source approach, we ensure our own code and tools are transparent and auditable.

Architecture#

Cape Privacy provides Cape Coordinator, to manage policy and users. This will interact with the Cape Privacy libraries (such as Cape Python) through a workers interface, and with your own data services through an API.

Data flow#

Data will flow between the following elements of Cape Privacy's architecture:

  • Cape workers pass policy information to Cape libraries.
  • Cape Coordinator has an internal policy management workflow, from a request for new policy, through collaborating review, to using the policy to control how the libraries transform your data.
  • The Cape API will exchange information relevant to auditors with your own monitoring tools.

Upcoming features#

Cape Python#

  • Reversible tokenisation: allow reversing of tokenization to reveal the raw value.
  • Policy audit logging: create logging hooks to allow audit logs for policy downloads and usage in Cape Python.
  • Expand pipeline integrations: add Apache Beam, Apache Flink, Apache Arrow Flight, or Dask integration as another pipeline we can support, either as part of Cape Python or in its own separate project.

Cape Core#

  • Audit logging configuration: set up configuration for how and where you log actions in Cape Coordinator, such as project and policy creation, user changes, and user actions in Cape.
  • Governance tooling: integrate basic data governance information to be used within Cape Coordinator for writing better policy, with a possible integration with Apache Atlas or other open-source governance tools.
  • Pipeline orchestrator integration: ability to connect with Spark orchestration tools (such as YARN, Mesos, and Airflow) and pull information on jobs that are running for easier management of running Spark installations.