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Access a confidential dataset
In this tutorial, you will learn how to leverage an encrypted dataset by using the
IEXEC_DATASET_FILENAME environment variable in your application.The Gramine TEE framework does not support encrypted datasets at the moment. This page is only applicable to the Scone TEE framework.
Prerequisites:
Trusted Execution Environments offer a huge advantage from a security perspective. They guarantee that the behavior of execution does not change even when launched on an untrusted remote machine. The data inside this type of environment is also protected, which allows its monetization while preventing leakage.
With iExec, it is possible to authorize only applications you trust to use your datasets and get paid for it. Data is encrypted using standard encryption mechanisms and the plain version never leaves your machine. The encrypted version is made available for usage and the encryption key is pushed into the SMS. After you deploy the dataset on iExec it is you, and only you, who decides which application is allowed to get the secret to decrypt it.
Datasets are only decrypted inside authorized enclaves and never leave them. The same thing applies to secrets.
Your secrets are transferred with the SDK from your machine to the SMS over a TLS channel.
Let's see how to do all of that!
Before starting, let's make sure we are inside the
~/iexec-projects folder previously created during the quick start tutorial.cd ~/iexec-projects
mkdir tee-dataset-app && cd tee-dataset-app
iexec init --skip-wallet
Init the dataset configuration.
iexec dataset init --encrypted
This command will create the
datasets/encrypted, datasets/original and .secrets/datasets folders. A new dataset section will be added to the iexec.json file as well..
├── datasets
│ ├── encrypted
│ └── original
└── .secrets
└── datasets
We will create a dummy file that has
"Hello, world!" as content inside datasets/original. Alternatively, you can put your own dataset file.echo "Hello, confidential world!" > datasets/original/my-first-dataset.txt
datasets
├── encrypted
└── original
└── my-first-dataset.txt
Now run the following command to encrypt the file:
iexec dataset encrypt
iexec dataset encrypt will output a checksum, keep this value for a later use.datasets
├── encrypted
│ └── my-first-dataset.txt.enc
└── original
└── my-first-dataset.txt
As you can see, the command generated the file
datasets/encrypted/my-first-dataset.txt.enc. That file is the encrypted version of your dataset, you should push it somewhere accessible because the worker will download it during the execution process. You will enter this file's URI in the iexec.jsonfile (multiaddr attribute) when you will deploy your dataset. Make sure that the URI is a DIRECT download link (not a link to a web page for example).You can use Github for example to publish the file but you should add /raw/ to the URI like this: https://github.com/<username>/<repo>/raw/master/my-first-dataset.txt.enc
The file
.secrets/datasets/my-first-dataset.txt.key is the encryption key, make sure to back it up securely. The file .secrets/datasets/dataset.key is just an "alias" in the sense that it is the key of the last encrypted dataset..secrets
└── datasets
├── dataset.key
└── my-first-dataset.txt.key
Fill in the fields of the
iexec.json file. Choose a name for your dataset, put the encrypted file's URI in multiaddr (the URI you got after publishing the file) and fill the checksum field. The checksum of the dataset consists of a 0x prefix followed by the sha256sum of the dataset. This checksum is printed when running the iexec dataset encrypt command. If you missed it, you can retrieve the sha256sum of the dataset by running sha256sum datasets/encrypted/my-first-dataset.txt.enc.$ cat iexec.json
{
"description": "My iExec ressource description...",
...
"dataset": {
"owner": "0x-your-wallet-address",
"name": "Encrypted hello world dataset",
"multiaddr": "/ipfs/QmW2WQi7j6c7UgJTarActp7tDNikE4B2qXtFCfLPdsgaTQ",
"checksum": "<0x-sha256sum-of-the-dataset>" // starts with 0x
}
}
To deploy your dataset run:
iexec dataset deploy
You will get a hexadecimal address for your deployed dataset. Use that address to push the encryption key to the SMS so it is available for authorized applications.
For simplicity, we will use the dataset with a TEE-debug app on a debug workerpool. The debug workerpool is connected to a debug Secret Management Service so we will send the dataset encryption key to this SMS (this is fine for debugging but do not use to store production secrets).
iexec dataset push-secret
iexec dataset check-secret
We saw in this section how to encrypt a dataset and deploy it on iExec. In addition, we learned how to push the encryption secret to the SMS. Now we need to build the application that is going to consume this dataset.
For demo purposes, we omitted some development best practices in these examples.
Make sure to check your field's best practices before going to production.
Let's create a directory tree for this app in
~/iexec-projects/.cd ~/iexec-projects/tee-dataset-app
mkdir src
touch Dockerfile
touch sconify.sh
chmod +x sconify.sh
In the folder
src/ create the file app.js or app.py then copy this code inside:The application reads the content of the dataset and writes it into the result's folder (in an artistic way using Figlet):
Javascript
Python
src/app.js
const fsPromises = require("fs").promises;
const figlet = require("figlet");
(async () => {
try {
const iexecOut = process.env.IEXEC_OUT;
const iexecIn = process.env.IEXEC_IN;
const datasetFileName = process.env.IEXEC_DATASET_FILENAME;
// Use some confidential assets
let text = "";
try {
const confidentialFile = await fsPromises.readFile(
`${iexecIn}/${datasetFileName}`
);
text = figlet.textSync(confidentialFile.toString());
} catch (e) {
console.log("confidential file does not exist");
}
// Append some results
await fsPromises.writeFile(`${iexecOut}/result.txt`, text);
console.log(text);
// Declare everything is computed
const computedJsonObj = {
"deterministic-output-path": `${iexecOut}/result.txt`,
};
await fsPromises.writeFile(
`${iexecOut}/computed.json`,
JSON.stringify(computedJsonObj)
);
} catch (e) {
console.log(e);
process.exit(1);
}
})();
src/app.py
import json
import os
from pyfiglet import Figlet
iexec_out = os.environ['IEXEC_OUT']
iexec_in = os.environ['IEXEC_IN']
dataset_filename = os.environ['IEXEC_DATASET_FILENAME']
text = ''
# Check the confidential file exists and open it
try:
dataset_file = open(iexec_in + '/' + dataset_filename, 'r')
dataset = dataset_file.read()
text = Figlet().renderText(dataset)
except OSError:
print('confidential file does not exists')
exit(1)
print(text)
# Append some results in /iexec_out/
with open(iexec_out + '/result.txt', 'w+') as fout:
fout.write(text)
# Declare everything is computed
with open(iexec_out + '/computed.json', 'w+') as f:
json.dump({"deterministic-output-path": iexec_out + '/result.txt'}, f)
Build the Docker iamge:
docker build . --tag <docker-hub-user>/hello-world-with-dataset:1.0.0
Update the
sconify.sh script with the variables as follow:# declare related variables
IMG_NAME=tee-scone-hello-world-with-dataset
IMG_FROM=<docker-hub-user>/hello-world-with-dataset:1.0.0
IMG_TO=<docker-hub-user>/${IMG_NAME}:1.0.0-debug
Run the
sconify.sh script to build the Scone TEE application:./sconify.sh
docker push <docker-hub-user>/tee-scone-hello-world-with-dataset:1.0.0-debug
At this stage, your application is ready to be tested on iExec.
Specify the tag
--tag tee,scone and the dataset to use --dataset <datasetAddress> in iexec app run command to run a tee app with a dataset.One last thing, in order to run a TEE-debug app you will also need to select a debug workerpool, use the debug workerpool
debug-v8-bellecour.main.pools.iexec.eth.You are now ready to run the app
iexec app run <appAddress> \
--tag tee,scone \
--dataset <datasetAddress> \
--workerpool debug-v8-bellecour.main.pools.iexec.eth \
--watch
Thanks to the explained confidential computing workflow, you now know how to use an encrypted dataset in a Confidential Computing application.
To go further, check out how to:
Last modified 19d ago