PyDP
PrivacyEngCollabSpace
| PyDP | PrivacyEngCollabSpace | |
|---|---|---|
| 1 | 1 | |
| 483 | 224 | |
| 2.3% | 3.6% | |
| 7.0 | 7.3 | |
| 4 months ago | 13 days ago | |
| Python | Python | |
| Apache License 2.0 | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PyDP
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How to make the medical data in order to protect the data privacy?
As the title mentioned, I studied some tutorials about differential privacy and the examples of PyDP, but they only deal with simple cases(structure text). Which paper/direction I should focus on if I want to make the unstructured medical data private? Is it possible to make the data private with some preprocessing before I feed the data into the model? A naive idea is find out the sensitive part(ex : name), change them to non sensitive text manually. Thanks
PrivacyEngCollabSpace
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What format / templates do you (CISOs/ISOs) use for your risk assessments of the org?
I would look into some NIST-provided tools like this one: https://github.com/usnistgov/PrivacyEngCollabSpace/tree/master/tools/risk-assessment/NIST-Privacy-Risk-Assessment-Methodology-PRAM. Haven't used it myself but it looks like it might fit your use-case.
What are some alternatives?
mailjet-apiv3-python - [API v3] Python Mailjet wrapper
presidio - Context aware, pluggable and customizable data protection and de-identification SDK for text and images
differential-privacy-library - Diffprivlib: The IBM Differential Privacy Library
CuVec - Unifying Python/C++/CUDA memory: Python buffered array ↔️ `std::vector` ↔️ CUDA managed memory
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Ciphey - ⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡
gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.
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tern - Tern is a software composition analysis tool and Python library that generates a Software Bill of Materials for container images and Dockerfiles. The SBOM that Tern generates will give you a layer-by-layer view of what's inside your container in a variety of formats including human-readable, JSON, HTML, SPDX and more.
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