Update islet project readmes for /Samsung/ -> /islet-project/ move

Also cleanup trailing whitespaces. Signed-off-by: Lukasz Pawelczyk <l.pawelczyk@samsung.com>
islet-project · Jan 22, 2024 · 1ddfc4c · 1ddfc4c
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diff --git a/README.md b/README.md
@@ -1,26 +1,26 @@
-<p align="center"><img src="https://github.com/Samsung/islet/blob/main/doc/res/logo-title.jpg?raw=true" height="100px"></p>
+<p align="center"><img src="https://github.com/islet-project/islet/blob/main/doc/res/logo-title.jpg?raw=true" height="100px"></p>
 
-Islet is an open-source software project written in Rust that enables confidential computing 
+Islet is an open-source software project written in Rust that enables confidential computing
 on ARM architecture devices using the ARMv9 CCA.
-The primary objective of Islet is to enable on-device confidential computing 
-and protect user privacy on end user devices. 
+The primary objective of Islet is to enable on-device confidential computing
+and protect user privacy on end user devices.
 
-While current confidential computing solutions mainly focus on server-side 
-protection,  it is equally important to safeguard user information at the user 
+While current confidential computing solutions mainly focus on server-side
+protection,  it is equally important to safeguard user information at the user
 device level  since that is where private data collection initially occurs.
-Furthermore, as more and more users rely on privacy apps such as private 
-messengers,  secure emails, password managers, and web browsers with privacy 
+Furthermore, as more and more users rely on privacy apps such as private
+messengers,  secure emails, password managers, and web browsers with privacy
 settings,  there is a growing need to ensure privacy on user devices.
 Islet, an open-source project, addresses this need by providing a platform
- for ARM-based confidential computing. 
+ for ARM-based confidential computing.
 
-Enabling CC on user devices will not only establish end-to-end CC throughout 
-the entire data processing path, 
-but it will also help create a secure computation model 
-that enables processing of user private data on the user device 
-using the same components that previously were employed at the server side 
-without disclosing business logic. 
-Furthermore, on-device confidential computing will be a key enabler for 
+Enabling CC on user devices will not only establish end-to-end CC throughout
+the entire data processing path,
+but it will also help create a secure computation model
+that enables processing of user private data on the user device
+using the same components that previously were employed at the server side
+without disclosing business logic.
+Furthermore, on-device confidential computing will be a key enabler for
 machine-to-machine computing without the need for server intervention
 
 ## Feature Overview
@@ -31,38 +31,38 @@ machine-to-machine computing without the need for server intervention
 
 ## Overall Architecture
 
-Islet provides a platform for running virtual machines (VMs) 
-confidentially, with standard SDKs for easy integration with other confidential 
-computing frameworks at upper layers. 
-The platform consists of two key components: 
-the Islet Realm Management Monitor (Islet-RMM) and Islet Hardware Enforced Security (Islet-HES).  
+Islet provides a platform for running virtual machines (VMs)
+confidentially, with standard SDKs for easy integration with other confidential
+computing frameworks at upper layers.
+The platform consists of two key components:
+the Islet Realm Management Monitor (Islet-RMM) and Islet Hardware Enforced Security (Islet-HES).
 
-- `Islet RMM` operates at EL2 in the Realm world on the application processor cores 
-and manages the confidential VMs, known as realms. 
-- On the other hand, `Islet HES` performs device boot measurement, generates 
-platform attestation reports, and manages sealing key functionality within a secure 
+- `Islet RMM` operates at EL2 in the Realm world on the application processor cores
+and manages the confidential VMs, known as realms.
+- On the other hand, `Islet HES` performs device boot measurement, generates
+platform attestation reports, and manages sealing key functionality within a secure
 hardware IP apart from the main application processor.
 
 ![islet-overview](doc/res/overview.png)
 
-In designing Islet, we aim to to address the current security challenges in confidential 
+In designing Islet, we aim to to address the current security challenges in confidential
 computing technologies right from the very beginning.
-To ensure that our software is built with safety in mind, we have chosen to use the 
-Rust programming language, known for its unique security model that ensures memory 
-safety and concurrency safety. 
-Moving forward, we also plan to incorporate formal 
+To ensure that our software is built with safety in mind, we have chosen to use the
+Rust programming language, known for its unique security model that ensures memory
+safety and concurrency safety.
+Moving forward, we also plan to incorporate formal
 verification techniques to further enhance the security of our design and implementation.
 
 For more information, please visit our [developer site](https://islet-project.github.io/islet/).
 
 ## A demo video (Confidential ML)
 
-![this page](https://github.com/Samsung/islet/raw/main/examples/confidential-ml/video/confidential_ml.gif)
+![this page](https://github.com/islet-project/islet/raw/main/examples/confidential-ml/video/confidential_ml.gif)
 
 - This video shows how ISLET achieves an end-to-end confidential machine learning with a chat-bot scenario.
 - This video flows as follows.
   1. It starts with a slide that describes all components involved in this demo. All components will run on confidential computing platforms.
   2. (*feed an ML model*) The model provider feeds the ML model into the ML server. This is done through a secure channel established with the aid of the certifier framework.
   3. (*run a coding assistant*) A mobile device user asks a chat-bot application that runs on ISLET for generating a function. And then, that request is passed on to the ML server through a secure channel. Finally, the user can see the result (i.e., function).
   4. (*launch a malicious server*) This time, we launch a malicious server to show a failure case. When it attempts to join the certifier service (on the right side of the screen), it will not pass authentication as it results in a different measurement. Therefore, the malicious server cannot interact with the mobile device user in the first place.
-- To download this video, click [here](https://github.com/Samsung/islet/raw/main/examples/confidential-ml/video/confidential_ml.mp4).
+- To download this video, click [here](https://github.com/islet-project/islet/raw/main/examples/confidential-ml/video/confidential_ml.mp4).
diff --git a/doc/getting-started/app-dev.md b/doc/getting-started/app-dev.md
@@ -43,7 +43,7 @@ Sealing result Ok(())
 
 ## Example code snippet
 Below is code snippet of the example.
-You can refer [the whole example code](https://github.com/Samsung/islet/blob/main/sdk/examples/simulated.rs).
+You can refer [the whole example code](https://github.com/islet-project/islet/blob/main/sdk/examples/simulated.rs).
 
 ```rust
 use islet_sdk::prelude::*;
@@ -60,4 +60,3 @@ let sealed = seal(plaintext)?;
 let unsealed = unseal(&sealed)?;
 assert_eq!(plaintext, &unsealed[..]);
 ```
-
diff --git a/doc/getting-started/plat-dev.md b/doc/getting-started/plat-dev.md
@@ -4,7 +4,7 @@ Platform components include from Realm Management Monitor(RMM) to Realm.
 
 `Islet` provides Rust-based RMM and scripts to compose Confidential Computing Platform.
 You can explore CCA platform with our scripts and
-powerful [third-party projects](https://github.com/Samsung/islet/tree/main/third-party).
+powerful [third-party projects](https://github.com/islet-project/islet/tree/main/third-party).
 
 ## Setting build environment
 
@@ -39,8 +39,8 @@ $ LD_LIBRARY_PATH=./ ./sdk-example-c
 ```
 
 ## Running a linux realm with a networking support and prebuilt examples
-See [examples](https://github.com/Samsung/islet/tree/main/examples).
-To get details about its network configuration, see [network.md](https://github.com/Samsung/islet/blob/main/doc/network.md)
+See [examples](https://github.com/islet-project/islet/tree/main/examples).
+To get details about its network configuration, see [network.md](https://github.com/islet-project/islet/blob/main/doc/network.md)
 
 ## Testing the realm features
 ```bash

diff --git a/doc/usecases/confidential_ml.md b/doc/usecases/confidential_ml.md
@@ -65,4 +65,4 @@ We see that this attack could be addressed to some extent by designing and imple
 ## Time to play around with real examples
 
 Anyone can try out what we've explained so far, that is to say, running traditional ML or federated learning on top of ISLET with simple ML models.
-Check out [this markdown file](https://github.com/Samsung/islet/tree/main/examples/confidential-ml) to play around with ISLET for confidential ML!
+Check out [this markdown file](https://github.com/islet-project/islet/tree/main/examples/confidential-ml) to play around with ISLET for confidential ML!
diff --git a/examples/confidential-ml/code_model.md b/examples/confidential-ml/code_model.md
@@ -1,7 +1,7 @@
 ## Try out confidential code generation in ML setting
 
 This section explains how to try out confidential code generation in ML setting. (For this model, FL is not supported)
-We prepare [a docker image](https://github.com/Samsung/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz) that contains everything needed to try out this example and it involves 4 different instances-- *certifier-service*, *runtime*, *model-provider*, *device*-- meaning that you need to open 4 terminals for each of them.
+We prepare [a docker image](https://github.com/islet-project/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz) that contains everything needed to try out this example and it involves 4 different instances-- *certifier-service*, *runtime*, *model-provider*, *device*-- meaning that you need to open 4 terminals for each of them.
 
 In this example, *device* is not involved in ML operations (inference and training), they just pass user-input on to *runtime* and then *runtime* does inference with the code model and give the result(code) back to *device*.
 The code model is a pre-trained model and *runtime* will not do training with user-input. This is the way that most state-of-the-art chatbots work these days.
@@ -16,7 +16,7 @@ the quality of the output might be low. See [this csv file](./model_provider/cod
 Before trying this example, please do the following first to import and run a docker image.
 (Note that this docker image is based on Ubuntu 22.04)
 ```
-$ wget https://github.com/Samsung/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz
+$ wget https://github.com/islet-project/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz
 $ gzip -d cca_ubuntu_release.tar.gz
 $ cat cca_ubuntu_release.tar | sudo docker import - cca_release:latest
 $ sudo docker run --net=host -it -d --name=cca_ubuntu_release cca_release /bin/bash
@@ -165,7 +165,7 @@ $ <browser> open a browser and go in http://localhost:8000
 $ <browser> type a request in the chatbox, such as "write a function to add two numbers",
   and then device(terminal-5) passes the request on to the runtime(terminal-3),
   and eventually, the chatbot in the browser will show the prediction (code) made by runtime.
-  
+
   Here is the code:
   int min(int a, int b) {
       return a > b ? b : a;
@@ -223,7 +223,7 @@ $ <browser> open a browser and go in http://193.168.10.15:8000
 $ <browser> type a request in the chatbox, such as "write a function to add two numbers",
   and then device(terminal-5) passes the request on to the runtime(terminal-3),
   and eventually, the chatbot in the browser will show the prediction (code) made by runtime.
-  
+
   Here is the code:
   int min(int a, int b) {
       return a > b ? b : a;

diff --git a/examples/confidential-ml/word_model.md b/examples/confidential-ml/word_model.md
@@ -1,7 +1,7 @@
 ## Try out confidential word prediction in ML setting
 
 This section explains how to try out confidential word prediction in ML setting.
-We prepare [a docker image](https://github.com/Samsung/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz) that contains everything needed to try out this example and it involves 5 different instances-- *certifier-service*, *runtime*, *model-provider*, *device1*, *device2*-- meaning that you need to open 5 terminals for each of them.
+We prepare [a docker image](https://github.com/islet-project/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz) that contains everything needed to try out this example and it involves 5 different instances-- *certifier-service*, *runtime*, *model-provider*, *device1*, *device2*-- meaning that you need to open 5 terminals for each of them.
 
 [TODO] Note that as of now we do not offer any convenient way to try out this example in your host machine directly instead of the docker image, as this example involves a lot of dependencies. Anyhow, we plan to support building and testing this example on the host PC in the near future.
 
@@ -35,7 +35,7 @@ $ <terminal-4: device1> ./run.sh 0.0.0.0 8125 word 0
    Prediction: abou{            # this is an initial prediction as a result of on-device inference
    Type correct answer: about   # provide a correct answer for training
    ...
-   ...                          # sends "about" to runtime. runtime does training with this data and sends a newly trained model to this device.                   
+   ...                          # sends "about" to runtime. runtime does training with this data and sends a newly trained model to this device.
    ...
    Type characters: abo         # type in "abo" again and see if it leads to "about" which is a correct word.
    Prediction: about            # shows a correct guess-!
@@ -107,7 +107,7 @@ $ <terminal-4: device1> ./run.sh 192.168.33.1 8125 word 0
    Prediction: abou{            # this is an initial prediction as a result of on-device inference
    Type correct answer: about   # provide a correct answer for training
    ...
-   ...                          # sends "about" to runtime. runtime does training with this data and sends a newly trained model to this device.                   
+   ...                          # sends "about" to runtime. runtime does training with this data and sends a newly trained model to this device.
    ...
    Type characters: abo         # type in "abo" again and see if it leads to "about" which is a correct word.
    Prediction: about            # shows a correct guess-!
@@ -116,7 +116,7 @@ $ <terminal-4: device1> ./run.sh 192.168.33.1 8125 word 0
 ## Try out confidential word prediction in FL setting
 
 This section explains how to try out confidential word prediction in FL setting. We make a simple word prediction model that is based on SimpleRNN of TensorFlow.
-We prepare [a docker image](https://github.com/Samsung/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz) that contains everything needed to try out this example and it involves 5 different instances-- *certifier-service*, *runtime*, *model-provider*, *device1*, *device2*-- meaning that you need to open 5 terminals for each of them.s
+We prepare [a docker image](https://github.com/islet-project/islet/releases/download/example-confidential-ml-v1.1/cca_ubuntu_release.tar.gz) that contains everything needed to try out this example and it involves 5 different instances-- *certifier-service*, *runtime*, *model-provider*, *device1*, *device2*-- meaning that you need to open 5 terminals for each of them.s
 
 [TODO] Note that as of now we do not offer any convenient way to try out this example in your host machine directly instead of the docker image, as this example involves a lot of dependencies. Anyhow, we plan to support building and testing this example on the host PC in the near future.
 
@@ -149,7 +149,7 @@ $ <terminal-4: device1> ./run.sh 0.0.0.0 8125 word 1
    Type correct answer: about   # provide a correct answer for training
    ...
    epoch: 90, loss: 0.000,0.000 # do on-device training
-   ...                          # wait for a global model from runtime                                        
+   ...                          # wait for a global model from runtime
    ...
 
 $ <terminal-5: device2> cd /islet/examples/confidential-ml/device
@@ -224,4 +224,4 @@ $ <terminal-5: device2> cd /shared/examples/confidential-ml/device
 $ <terminal-5: device2> ./init.sh 192.168.33.1
 $ <terminal-5: device2> ./run.sh 192.168.33.1 8126 word 1
   # test it the same way we did with "How to test with simulated enclave (no actual hardware TEE) on x86_64"
-``
+``
diff --git a/third-party/README.md b/third-party/README.md
@@ -1,5 +1,5 @@
 # Third-party projects
 
 ISLET uses several third-party projects for realm, normal-world and testing.
-Third-party projects are managed using `submodule` 
-which means they are forked from upstream to the branch of [ISLET-ASSET repo](https://github.com/Samsung/islet-asset).
+Third-party projects are managed using `submodule`
+which means they are forked from upstream to the branch of [ISLET-ASSET repo](https://github.com/islet-project/assets).