Lately, Iβve been working on #PrivacyPreservingML π I got looped in some projects after Apple launched AppTrackingTransparency (ATT) framework, requiring iOS apps to ask permission to share usersβ data w/ 3rd parties. This has triggered an industry-wide discussion on best practices to respect user privacy.
Lately, Iβve been working on #PrivacyPreservingML π
— Jigyasa Grover ξ¨ (@jigyasa_grover) July 11, 2022
I got looped in some projects after Apple launched ATT, requiring iOS apps to ask permission to share usersβ data w/ 3rd parties.
This has triggered an industry-wide discussion on best practices to respect user privacy. pic.twitter.com/Ne1aF3p9F9
ML models are as good as the data we feed them π£ In the online world, it's tempting to use data from a user's each & every move by sharing info across companies to make the models smarter -- in the guise of βserving the users betterβ. #DataCentricAI
But the use of every data point w/o protecting it has led to cyber attacks, reverse engineering, and violations of sensitive data like personal conversations, financial transactions, medical history, etc. This is where #PrivacyPreservingML comes into play!
To put it simply:
Privacy-Preserving ML = Data Privacy + Model Privacy
Data Privacy = Using authorized data + not exposing training data & its source + protecting input & output data. Check out this βsecret sharerβ paper -- certain sequences (for eg. credit card numbers, SSN) can be unintentionally memorized!
Model Privacy = Protect the model and its nuances (aka parameters) from the public eye. Apart from Intellectual Property loss, using it to reverse engineer inputs from the outputs can prove to be a dangerous problem in many sensitive situations.
There are many methods to ensure that the data cannot be stolen by a third party or how to ensure model privacy. Might come back to these #PrivacyPreservingML techniques some other time, until then CIAO ππ»
On that note, if you are someone who is still getting started on #DataForML and need a boost to your #MachineLearning dataset building skills using #Python and all things #OpenSource! Check out @DataForML on @amazon π