Update IRB readme

Signed-off-by: Marcus Brandenburger <[email protected]>

Author: mbrandenburger

samples/demos/irb/README.md

@@ -1,10 +1,69 @@
-# IRB approval sample
+# Institutional Review Board (IRB) Sample
 
-Get redis
+This demo implements an FPC-based IRB experiment approval services to protect the confidentiality of data being used in analytical experiments. 
+The problem addressed here is relevant in a healthcare context, where sensitive patient data is processed in clinical trails.    
+Multiple participants, namely, data providers, experimenter, and principal investigators are collaborating to conduct analytical research using sensitive data.  
+
+We show a prototype of an application that performs analytical experiments on data such that:
+- Any constraints on the use of the data are respected (consent).
+- The confidentiality of data and policies for its use is maintained.
+- Analytical experiments must be approved by an IRB which ensures that the description and implementation of the experiment are appropriate.
+- Analytical experiments are executed in an SGX container (Graphene) that can attest to the integrity of the computation.
+
+The demo was presented at the Hyperledger Global Forum (HLGF) 2021. Check the recordings on [youtube](https://www.youtube.com/watch?v=MU4BpZp8A1Y).
+
+Note that the demo source code here is a simplified version of the demo presented at HLGF21, to fully focus on the FPC implementation.
+In particular, the experiment is not protected with SGX and the WebUI component is not present.
+The full source code for the HLFG21 presentation is located in the [irb-demo](https://github.com/hyperledger/fabric-private-chaincode/tree/irb-demo/samples/demos/irb) branch.
+
+The IRB use case requires the interaction between the participants. 
+A typical application flow begins with the data registration and consent, following the approval protocol, and finally executing the experiment and publishing the results.
+We use [Fabric Smart Client](https://github.com/hyperledger-labs/fabric-smart-client) to implement the complex interactions. 
+
+## What the demo shows
+
+The demo runs in the terminal and demonstrates the application flow.
+In particular, the demo starts a Fabric network with multiple organizations participating and hosting fabric peers.
+The Fabric Smart Client installs the FPC Chaincode that implements the IRB experiment approval service.
+
+Once the network is ready, the application flow begins with the investigator, creating a new study.
+Next, the data provider registers and uploads new patient data to the systems, assigning the data to be used in the study.
+Now we have a study and data to process.
+
+The experimenter proceeds with creating a new experiment and asks the investigator for approval.
+Once the approval arrived, that is, the investigator has reviewed the experiment proposal and submitted an approval to the IRB experiment approval service,  the execution of the experiment is triggered.
+
+Finally, the experimenter requests an evaluation pack from the IRB experiment approval service, that contains the access information to collect the patient data.
+The evaluation pack is passed to the experiment instance, which fetches the patient data, performs the experiment computation, and returns the result.
+Note that the patient data is encrypted in a way that only an approved experiment instance can decrypt the data.
+More details on that, see the HLGF21 presentation.
+
+## Code structure
+- `/chaincode` contains the FPC chaincode that implements the IRV approval service
+- `/experimenter` contains the analytic experiment code based on [PyTorch](https://pytorch.org/).
+- `/pkg` contains implementations for various components, including crypto, container management, etc. 
+- `/protos` contains the message definitions used in this application
+- `/views` contains the protocol implementation for the data provider, experimenter, and investigator.
+- `irb_test.go` is the starting point of the demo.
+- `topology.go` defines the Fabric network used in this demo.
+
+## Setup
+
+The demo uses redis to store the data provided by the patients. Get a redis docker image.
 ```bash
 docker pull redis:latest
 ```
 
+Next, we build the components of the demo by running:
 ```bash
-make build test
-```
+make build
+```
+
+## Run the demo
+
+To run the demo just use the `test` target.
+You will see the output of the interaction between the participants in your terminal.
+
+```bash
+make test
+```