chore: update readme (#19)

Author: andrei-stoian-zama

COMPILING.md

@@ -0,0 +1,82 @@
+# Compiling app dependencies for iOS and Mac simulator
+
+## 1. Install Rust
+
+1. Install the latest Rust release (currently 1.81.0):
+```shell
+    curl https://sh.rustup.rs -sSf | sh
+``` 
+
+2.  Install extra target architectures (for iOS devices & iOS simulators running on Apple Silicon Macs):
+```shell
+    rustup target add aarch64-apple-ios aarch64-apple-ios-sim
+```
+
+3. Install nightly Rust toolchain (a TFHE-rs requirement):
+```shell
+rustup toolchain install nightly
+```
+
+4. Install Rust source so as to cross compile `std` lib (a TFHE-rs requirement):
+```shell
+rustup component add rust-src --toolchain nightly-aarch64-apple-darwin
+```
+
+## 2. Compile TFHE-rs for use in Swift
+
+1. Get TFHE-rs:
+```shell
+git clone --branch https://github.com/zama-ai/tfhe-rs.git
+```
+
+2. Compile for both iOS and iOS simulator targets:
+```shell
+RUSTFLAGS="" cargo +nightly build -Zbuild-std --release --features=aarch64-unix,high-level-c-api -p tfhe --target aarch64-apple-ios
+RUSTFLAGS="" cargo +nightly build -Zbuild-std --release --features=aarch64-unix,high-level-c-api -p tfhe --target aarch64-apple-ios-sim
+```
+
+3. Grab generated headers (.h):
+```shell
+cp $(TFHE_RS_PATH)/target/release/tfhe.h $(OUTPUT)/include/tfhe.h
+cp $(TFHE_RS_PATH)/target/aarch64-apple-ios/release/deps/tfhe-c-api-dynamic-buffer.h $(OUTPUT)/include/tfhe-c-api-dynamic-buffer.h
+```
+
+4. Create a Module Map:
+```shell
+touch $(OUTPUT)/include/module.modulemap
+```
+
+```swift
+module TFHE {
+  header "tfhe.h"
+  header "tfhe-c-api-dynamic-buffer.h"
+  export *
+}
+```
+
+5. Grab static librairies (.a):
+The iOS simulator library needs to be FAT, even if it contains one slice (you can also add an x86-64 slice later on):
+```shell
+lipo -create -output $(OUTPUT)/libtfhe-ios-sim.a $(TFHE_RS_PATH)/target/aarch64-apple-ios-sim/release/libtfhe.a
+```
+
+The iOS device library can be copied this way:
+```shell
+cp $(TFHE_RS_PATH)/target/aarch64-apple-ios/release/libtfhe.a $(OUTPUT)/libtfhe-ios.a
+```
+
+6. Package everything into an .xcframework:
+```shell
+xcodebuild -create-xcframework \
+    -library $(OUTPUT)/libtfhe-ios.a \
+    -headers $(OUTPUT)/include/ \
+    -library $(OUTPUT)/libtfhe-ios-sim.a \
+    -headers $(OUTPUT)/include/ \
+    -output $(OUTPUT)/TFHE.xcframework
+```
+
+Finally, move the `TFHE.xcframework` directory into the root directory of the iOS project. 
+
+## 3. Compile Concrete ML Extensions for use in Swift
+
+Follow the [instructions in the Concrete ML Extensions](https://github.com/zama-ai/concrete-ml-extensions?tab=readme-ov-file#from-source-for-ios) package to build additional Swift bindings that are used by **Data Vault**.

Docs/logo_dark.png

@@ -0,0 +1,28 @@
+BSD 3-Clause Clear License
+
+Copyright © 2024 ZAMA.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimer in the documentation and/or other
+materials provided with the distribution.
+
+3. Neither the name of ZAMA nor the names of its contributors may be used to endorse
+or promote products derived from this software without specific prior written permission.
+
+NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY THIS LICENSE.
+THIS SOFTWARE IS PROVIDED BY THE ZAMA AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
+ZAMA OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
+OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Docs/logo_light.png

@@ -1,142 +1,129 @@
-#  Welcome to FHE iOS Demo  
-
-Implements 3 iOS apps:
-1. **Zama Data Vault**: Encrypts sensitive user data (sleep, weight, profile info) using [TFHE-rs](https://github.com/zama-ai/tfhe-rs), and stores encrypted result in a shared folder for consumption by other apps. Clear, sensitive data never leaves device.
-2. **FHE Health**: Analyzes encrypted sleep & weight, producing graphs and insights, without ever accessing the clear data.
-3. **FHE Health**: Displays targeted ads based on encrypted profile info, without ever accessing the clear data.
-
-In app **FHE Health** and **FHE Health**, analysis and targeting is done privately on Zama's servers; you can monitor its activity at [https://api.zama.ai/logs](https://api.zama.ai/logs).
-
-# Useful Links
-- [Miro flow](https://miro.com/app/board/uXjVLpwDx3c=/)
-- [Canva screens](https://www.canva.com/design/DAGdBWLkdRE/UGMKBeI8vSnK1R-O5NFH2Q/edit)
-- [GitHub iOS Demo](https://github.com/zama-ai/fhe_ios_demo)
-- [GitHub DEAI](https://github.com/zama-ai/deai-dot-products)
-- [ Developer](https://developer.apple.com/account/)
-
-# References
-- [Zama Website](https://www.zama.ai)
-- [Zama THFE-rs](https://docs.zama.ai/tfhe-rs/get-started/quick_start)
-- [ Learn Swift Guide](https://docs.swift.org/swift-book/documentation/the-swift-programming-language)
-- [ Learn Swift UI Guide](https://developer.apple.com/tutorials/swiftui)
-- [Canva old exploration](https://www.canva.com/design/DAGUeG30ET0/Yy1lAaapPuLDaMJEukOM3Q/edit)
-- [Google Docs UI Feedbacks](https://docs.google.com/document/d/1VOvwO9D7kKKPg0mRWacHJUfFZYBwRZezs_tmD3H6384/)
-- [Huggingface Demo](https://huggingface.co/spaces/zama-fhe/encrypted_image_filtering)
-- [Tutorial: Calling a Rust library from Swift](https://medium.com/@kennethyoel/a-swiftly-oxidizing-tutorial-44b86e8d84f5)
-- [Minimize Rust binary size](https://github.com/johnthagen/min-sized-rust)
-- [Using imported C APIs in Swift](https://developer.apple.com/documentation/swift/imported-c-and-objective-c-apis)
+<p align="center">
+<!-- product name logo -->
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="Docs/logo_dark.png">
+  <source media="(prefers-color-scheme: light)" srcset="Docs/logo_light.png">
+  <img width=600 alt="Zama Concrete ML iOS Demos">
+</picture>
+</p>
+
+<hr>
+
+<p align="center">
+  <a href="https://docs.zama.ai/concrete-ml"> 📒 Documentation</a> | <a href="https://zama.ai/community"> 💛 Community support</a> | <a href="https://github.com/zama-ai/awesome-zama"> 📚 FHE resources by Zama</a>
+</p>
+
+<p align="center">
+  <a href="LICENSE"><img src="https://img.shields.io/badge/License-BSD--3--Clause--Clear-%23ffb243?style=flat-square"></a>
+  <a href="https://github.com/zama-ai/bounty-program"><img src="https://img.shields.io/badge/Contribute-Zama%20Bounty%20Program-%23ffd208?style=flat-square"></a>
+</p>
+
+## About
+
+### What is Concrete ML iOS Demos?
+
+This repository contains iOS applications that demonstrate 
+how FHE can help users securely get insights based on their personal
+data. The applications in this repository run on iPhones and connect to remote services that work with encrypted data. These services are implemented with **Concrete ML**.
+
+**Concrete ML** is a Privacy-Preserving Machine Learning (PPML) open-source set of tools built by [Zama](https://github.com/zama-ai). It simplifies the use of Fully Homomorphic Encryption (FHE) for data scientists so that they can automatically turn machine learning models into their homomorphic equivalents, and use them without knowledge of cryptography.
+
 
-# Installation Steps
+### Main features
 
-## Install Apple Tools
+The repository implements the **Data Vault** and several end-user demo applications. **Data Vault** is the main storage of sensitive information and two example apps that use sensitive data encrypted by the **Data Vault**.
+
+The **Data Vault** acts like a secure enclave: it encrypts sensitive user data (sleep, weight, profile info) and stores encrypted result in a shared folder for consumption by other apps. Human readable sensitive data never leaves device or the **Data Vault** app. 
+
+To display the insights or results obtained from encrypted data, end-user applications must request that **Data Vault** displays the information in secure widgets. 
+
+The following demo end-user applications are available:
+
+1. **FHE Health**: Analyzes sleep quality data and provides statistics about the user's weight, producing graphs and insights. The sleep tracking can be done by an iWatch using the dedicated [Sleep App](https://support.apple.com/guide/watch/track-your-sleep-apd830528336/watchos).
+1. **FHE Ads**: Displays targeted ads based on an encrypted user-profile. Internet advertising relies on behavioral profiling through cookies, but tracking user behavior without encryption has privacy risks. With FHE, a user can manually create their profile and ads can be matched to it without actually exposing the user-profile.
+
+For these demo end-user applications, analysis and processing of the encrypted information is done on Zama's servers. Server side functionality for these end-user applications is implemented in the [Server](Server/README.md) directory.
+
+The **Data Vault** uses [TFHE-rs](https://github.com/zama-ai/tfhe-rs) and  [Concrete ML Extensions](https://github.com/zama-ai/concrete-ml-extensions) to encrypt and decrypt data.
+
+## Setup
+
+### Install Apple Tools
 - macOS 15 Sequoia
 - Xcode 16.2 [from AppStore](https://apps.apple.com/fr/app/xcode/id497799835) or [developer.apple.com](https://developer.apple.com/download/applications/)
 - iOS 18.2 SDK (additional download from Xcode)
 
-## Install AdImages
+### Install AdImages
 - Simply unzip `QLAdsExtension/AdImages.zip' in place.
 
-## Having TFHE libraries for iOS and mac simulator
+### Compiling app dependencies for iOS and Mac simulator
 
-There are two ways to obtain those libraries:
-- the first one is the easiest: ask someone who has already built the libraries to send them to you; we don't store them in GitHub since they are about 340 MB, but clearly not everyone needs to build them
-- the second one is needed at least for new TFHE-rs versions, or when one can't receive binaries made from others
+#### Building libraries
 
-### Getting libraries from others
+The easiest way to build all dependencies is to execute [the dedicated script](./setup_tfhe_xcframework.sh). 
 
-Simply save `TFHE.xcframework in the root directory. Inside this framework, there should be:
-- `Info.plist`
-- `ios-arm64`
-- `ios-arm64-simulator`
+To manually build the libraries follow the instructions in the [compilation guide](./COMPILING.md). The main steps are:
 
-### Building libraries
-
-There are several steps involved:
-- Installing Rust
-- Compiling TFHE-rs
-
-#### Installing Rust
-
-- Install latest Rust release (currently 1.81.0):
-```shell
-    curl https://sh.rustup.rs -sSf | sh
-``` 
-
-- Install extra target architectures (for iOS devices & iOS simulators running on Apple Silicon Macs):
-```shell
-    rustup target add aarch64-apple-ios aarch64-apple-ios-sim
-```
-
-- Install nightly Rust toolchain (a TFHE-rs requirement):
-```shell
-rustup toolchain install nightly
-```
-
-- Install Rust source so as to cross compile `std` lib (a TFHE-rs requirement):
-```shell
-rustup component add rust-src --toolchain nightly-aarch64-apple-darwin
-```
-
-#### Compiling TFHE-rs for use in Swift.
-
-##### Get TFHE-rs (currently 0.7.3):
-```shell
-git clone --branch tfhe-rs-0.7.3 https://github.com/zama-ai/tfhe-rs.git
-```
-
-##### Compile for both iOS and iOS simulator targets:
-```shell
-RUSTFLAGS="" cargo +nightly build -Zbuild-std --release --features=aarch64-unix,high-level-c-api -p tfhe --target aarch64-apple-ios
-RUSTFLAGS="" cargo +nightly build -Zbuild-std --release --features=aarch64-unix,high-level-c-api -p tfhe --target aarch64-apple-ios-sim
-```
-
-##### Grab generated headers (.h):
-```shell
-cp $(TFHE_RS_PATH)/target/release/tfhe.h $(OUTPUT)/include/tfhe.h
-cp $(TFHE_RS_PATH)/target/aarch64-apple-ios/release/deps/tfhe-c-api-dynamic-buffer.h $(OUTPUT)/include/tfhe-c-api-dynamic-buffer.h
-```
-
-##### Create a Module Map:
-```shell
-touch $(OUTPUT)/include/module.modulemap
-```
-
-```swift
-module TFHE {
-  header "tfhe.h"
-  header "tfhe-c-api-dynamic-buffer.h"
-  export *
-}
-```
-
-##### Grab Static Librairies (.a):
-The iOS simulator library needs to be FAT, even if it contains one slice (you can also add an x86-64 slice later on):
-```shell
-lipo -create -output $(OUTPUT)/libtfhe-ios-sim.a $(TFHE_RS_PATH)/target/aarch64-apple-ios-sim/release/libtfhe.a
-```
-
-The ios device library can be copied as is:
-```shell
-cp $(TFHE_RS_PATH)/target/aarch64-apple-ios/release/libtfhe.a $(OUTPUT)/libtfhe-ios.a
-```
-
-##### Package everything into an .xcframework:
-```shell
-xcodebuild -create-xcframework \
-    -library $(OUTPUT)/libtfhe-ios.a \
-    -headers $(OUTPUT)/include/ \
-    -library $(OUTPUT)/libtfhe-ios-sim.a \
-    -headers $(OUTPUT)/include/ \
-    -output $(OUTPUT)/TFHE.xcframework
-```
-
-##### Save
-
-Finally, move the `TFHE.xcframework` directory into the root directory of the iOS project. Inside this directory, there should be:
+1. [Install Rust](COMPILING.md#1-install-rust)
+1. [Compile TFHE-rs](COMPILING.md#2-compile-tfhe-rs-for-use-in-swift) 
+1. [Compile Concrete ML Extensions](COMPILING.md#3-compile-concrete-ml-extensions-for-use-in-swift)
+
+#### Using pre-built TFHE-rs libraries
+
+Instead of building the `TFHE.xcframework` from scratch, you can use a previously built version. Simply save `TFHE.xcframework` in the root directory. Inside this framework, there should be:
 - `Info.plist`
 - `ios-arm64`
 - `ios-arm64-simulator`
 
+## Data Vault and end-user application compilation
+
+Now you can open your Xcode IDE, open this directory and start building the apps.
+
+## End-user Application Server
+This repo also contains the backend implementations of the end-user applications. See the [server readme](Server/README.md) for more details on how to run these backends. 
+
+## Resources
+- [Tutorial: Calling a Rust library from Swift](https://medium.com/@kennethyoel/a-swiftly-oxidizing-tutorial-44b86e8d84f5)
+- [Minimize Rust binary size](https://github.com/johnthagen/min-sized-rust)
+- [Using imported C APIs in Swift](https://developer.apple.com/documentation/swift/imported-c-and-objective-c-apis)
+- [Concrete ML Documentation](https://docs.zama.ai/concrete-ml)
+
+## License
+
+This software is distributed under the **BSD-3-Clause-Clear** license. Read [this](LICENSE) for more details.
+
+## FAQ
+
+**Is Zama’s technology free to use?**
+
+> Zama’s libraries are free to use under the BSD 3-Clause Clear license only for development, research, prototyping, and experimentation purposes. However, for any commercial use of Zama's open source code, companies must purchase Zama’s commercial patent license.
+>
+> All our work is open source and we strive for full transparency about Zama's IP strategy. To know more about what this means for Zama product users, read about how we monetize our open source products in [this blog post](https://www.zama.ai/post/open-source).
+
+**What do I need to do if I want to use Zama’s technology for commercial purposes?**
+
+> To commercially use Zama’s technology you need to be granted Zama’s patent license. Please contact us at hello@zama.ai for more information.
+
+**Do you file IP on your technology?**
+
+> Yes, all of Zama’s technologies are patented.
+
+**Can you customize a solution for my specific use case?**
+
+> We are open to collaborating and advancing the FHE space with our partners. If you have specific needs, please email us at hello@zama.ai.
+
+<p align="right">
+  <a href="#about" > ↑ Back to top </a>
+</p>
+
+## Support
+
+<a target="_blank" href="https://zama.ai/community-channels">
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="https://github.com/zama-ai/concrete-ml/assets/157474013/86502167-4ea4-49e9-a881-0cf97d141818">
+  <source media="(prefers-color-scheme: light)" srcset="https://github.com/zama-ai/concrete-ml/assets/157474013/3dcf41e2-1c00-471b-be53-2c804879b8cb">
+  <img alt="Support">
+</picture>
+</a>
 
-# Running the Server
-Follow steps in Server/README.md
+🌟 If you find this project helpful or interesting, please consider giving it a star on GitHub! Your support helps to grow the community and motivates further development.