4 ms·

Privacy Enhancing Technologies Decision Tree

59 points by pthaine 6y ago

pthaine 6y ago
For devs & tech team managers wondering how to integrate privacy into their software pipeline.
WayToDoor 6y ago
Url should be changed from the medium post to https://www.private-ai.ca/PETs_Decision_Tree.png https://www.private-ai.ca/PETs_Decision_Tree.png
- wtallis 6y ago
  No. Delivering this content as a 3.8MB PNG is absurd. This would be a few kB as SVG, and even that's probably more than this content is worth.
  - WayToDoor 6y ago
    Yes, it could be served as an SVG. However, going to a medium page that just redirects you to this link without any explanation is not ideal either. A link change would still be better.
rhindi 6y ago
Nice overview! A few comments: - homomorphic encryption is much much faster now, so the latency argument against won’t hold much longer - with multi-key FHE, you could replace MPC, without the integration complexity and increased bandwidth cost - Trusted Execution Environments are not about protecting user data (that’s the purpose of FHE) but rather about protecting the software itself from people having access to the physical machine. An example would be running a sensitive ML model in the cloud: you would want to use FHE to process the user data encrypted, inside an TEE that would protect your model from the cloud vendor.
- pthaine 6y ago
  Thanks for your comments! - homomorphic encryption is much much faster now, so the latency argument against won’t hold much longer --> I can see that as being true for private libraries, hopefully this will become reality soon once public libraries integrate GPU or FPGA support - with multi-key FHE, you could replace MPC, without the integration complexity and increased bandwidth cost -> Good point! Multi-key FHE is still pretty tricky to implement, however. Though the research is getting there! - Trusted Execution Environments are not about protecting user data (that’s the purpose of FHE) but rather about protecting the software itself from people having access to the physical machine. An example would be running a sensitive ML model in the cloud: you would want to use FHE to process the user data encrypted, inside an TEE that would protect your model from the cloud vendor. -> It can be and is used for both code protection and user data protection (e.g., for authentication)

59 points by pthaine 6y ago

For devs & tech team managers wondering how to integrate privacy into their software pipeline.

Url should be changed from the medium post to https://www.private-ai.ca/PETs_Decision_Tree.png https://www.private-ai.ca/PETs_Decision_Tree.png

wtallis 6y ago
No. Delivering this content as a 3.8MB PNG is absurd. This would be a few kB as SVG, and even that's probably more than this content is worth.
- WayToDoor 6y ago
  Yes, it could be served as an SVG. However, going to a medium page that just redirects you to this link without any explanation is not ideal either. A link change would still be better.

rhindi 6y ago

Nice overview! A few comments: - homomorphic encryption is much much faster now, so the latency argument against won’t hold much longer - with multi-key FHE, you could replace MPC, without the integration complexity and increased bandwidth cost - Trusted Execution Environments are not about protecting user data (that’s the purpose of FHE) but rather about protecting the software itself from people having access to the physical machine. An example would be running a sensitive ML model in the cloud: you would want to use FHE to process the user data encrypted, inside an TEE that would protect your model from the cloud vendor.

pthaine 6y ago
Thanks for your comments! - homomorphic encryption is much much faster now, so the latency argument against won’t hold much longer --> I can see that as being true for private libraries, hopefully this will become reality soon once public libraries integrate GPU or FPGA support - with multi-key FHE, you could replace MPC, without the integration complexity and increased bandwidth cost -> Good point! Multi-key FHE is still pretty tricky to implement, however. Though the research is getting there! - Trusted Execution Environments are not about protecting user data (that’s the purpose of FHE) but rather about protecting the software itself from people having access to the physical machine. An example would be running a sensitive ML model in the cloud: you would want to use FHE to process the user data encrypted, inside an TEE that would protect your model from the cloud vendor. -> It can be and is used for both code protection and user data protection (e.g., for authentication)