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@Confidate

ConfiDate

ConfiDate

Infrastructure for private content personalisation based on user-private data.

What if the data on your phone, such as your selfie in front of Eiffel Tower or you going to an Art Museum could help you get lower credit loans and at the same time find the perfect date night partner? Your project aims to get more use of your personal data while keeping it strictly private, without ever leaving your device.

We use ZK ML to generate summary over your data on-device (iPhone), as well as a proof that this summary is correct. The summary can be:

  • How likely would you return a loan
  • What personality you have in dating
  • What dining food would you enjoy

We then expect that this data can be used to personalised the experience of the user and make their services even better, without compromising, and actually improving privacy. No longer need to send your data off-device!

Presentation of our project

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Solution

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Main repository

confidate-demoapp-ios

Library for making client-side proving on mobile simple

Python tool to transpile a tf.keras model into a circom circuit

Contacts:

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  1. confidate-demoapp-ios confidate-demoapp-ios Public

    Swift

  2. mopro mopro Public

    Forked from zkmopro/mopro

    Making client-side proving on mobile simple.

    Swift

  3. keras2circom_transpile keras2circom_transpile Public

    Forked from socathie/keras2circom

    Python tool to transpile a tf.keras model into a circom circuit

    Jupyter Notebook

  4. .github .github Public

  5. web3modal-swift web3modal-swift Public

    Forked from WalletConnect/web3modal-swift

    Swift

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