Workflow Applications Using Fortanix Enclave OS - SGX

NOTE
Download the Python script for use with this example and unzip the tar file into your working directory. It is best to use the scripts provided in the tar file instead of copying the scripts outlined in the example below to avoid potential formatting issues.

1.0 Introduction

Workflow graphs are maps that show how generic applications are connected to datasets and other generic applications. These are collaborative objects where multiple users can provide their own objects and approvals. For more details refer to the User Guide: Workflows.

This article describes how to create and deploy Fortanix CCM Workflows using a simple data processing example.

1.1 A Research Portal for Patient Data

This example demonstrates a Fortanix CCM workflow with Python scripts deployed securely using Fortanix Enclave OS.

The Workflow provides the most common diseases from patient data.

For input data, a synthetic file containing the medical history of fictive patients is used.

The data used for this example is available from https://synthea.mitre.org/downloads.

File: 1K Sample Synthetic Patient Records, CSV[mirror]: 9 MB

This is made available via:
Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall,
Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan,
Synthea: An approach, method, and software mechanism for generating synthetic patients
and the synthetic electronic health care record, Journal of the American Medical
Informatics Association, Volume 25, Issue 3, March 2018,
Pages 230–238, https://doi.org/10.1093/jamia/ocx079

The input and output dataset represents patient information collected by a different healthcare provider and contains the following elements:

  • Patient demographic information
  • Patient conditions
  • Patient encounters
  • Patient medications

1.2 Prerequisites

  • Set up a default config for Amazon Web Service (AWS) with your Access Key/Secret Key.
  • Install Boto using the following command:
    sudo apt-get install python3-boto3

2.0 Create CCM Workflow

2.1 Sign Up and Log In

  1. Sign in to Fortanix CCM using the URL: https://ccm.fortanix.com/. LoginPage.png
    Figure 1: Sign Up and Log In
    For more details, refer to Sign Up.
  2. Select an account once you have logged into Fortanix CCM. If you do not have an account create a new account. For more details, refer to the CCM Quickstart Guide.
  3. Create a CCM group for the application, datasets, and workflow. For more details, refer to the CCM Quickstart Guide

2.2 Invite Users to Join CCM

An Administrator invites users who are Data Owners, App Owners, and Output Owners to join a CCM account and create a workflow graph.

To invite users, click the Users menu item and in the Users page, click INVITE USER to invite new users to the account.

invite-user-landing-screen.png Figure 2: Invite Users

For more details, refer to User's Guide Invite Users.

The new users (Data Owners and App Owners) must join the CCM account and create Datasets and App Configs as described in the following sections.

2.3 Create Input and Output Datasets

Datasets are the definitions containing the location and access credentials of the data that allow the Enclave OS in the workflow to download the data and upload the data. In this example, we use the AWS S3 bucket to:

  • Store an encrypted file that will be downloaded and decrypted by the enclave OS.
  • Upload an encrypted file using the credentials provided in the dataset.

Create an S3 bucket with a directory that is accessible using a URL. 

2.3.1 Input User (Data Provider)

Consider that the Data Owner user has access to sensitive information and wants to allow an Application Owner to process this information.
This sensitive data is stored in a conditions.csv file.

In this example, the file is encrypted, uploaded to a storage solution (AWS S3), and a dataset is configured with credentials and an encryption key for enclave access/processing:

  1. If you haven't already, download and untar this tar ball workflow-scripts.tar.gzfile:
    tar xvfz workflow-scripts.tar.gz
  2. Obtain a copy of the CSV sample data (also available here: https://synthea.mitre.org/downloads).
    wget https://synthetichealth.github.io/synthea-sample-data/downloads/synthea_sample_data_csv_apr2020.zip
    unzip synthea_sample_data_csv_apr2020.zip
    You will only be using the file csv/conditions.csv file to encrypt this file, generate a key locally, encrypt the file and store the key in a KMS securely.
    1. Run the following command to generate an encryption key:
      xxd -u -l 32 -p /dev/random | tr -d '\n' > ./key.hex
    2. Use aes_256_gcm.py script included in the tar file that you downloaded. Run the following command to encrypt the file:
      ./workflow_scripts/aes_256_gcm.py enc -K ./key.hex -in ./csv/conditions.csv -out ./conditions.csv.enc
  3. Run the following command to upload the encrypted file to a secure storage location such as AWS S3:
    aws s3 --profile upload cp ./conditions.csv.enc <s3-directory>
  4. Generate a pre-signed URL to access the file. This is done to avoid inserting the whole AWS SDK in the example.
    Use the presign.py script included in the tar file and run the following command:
    ./presign.py default download <s3-directory> conditions.csv.enc 86400
    Where S3-directory is the directory of your S3 bucket directory.
    As an example, the output from the above command will be as shown below:
    https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc?AWSAccessKeyId=<key>Signature=PcpH99nszG2%2Fv85z4IbgwgVDywc%3D&Expires=1613817035
    The output consists of two parts:
    • The location: https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc
    • Query parameters: AWSAccessKeyId=<key>&Signature=PcpH99nszG2%2Fv85z4IbgwgVDywc%3D&Expires=1613817035
      The query parameters must be base64 encoded for the dataset definition using the following command:
      echo -n ‘AWSAccessKeyId=<key>Signature=PcpH99nszG2%2Fv85z4IbgwgVDywc%3D&Expires=1613817035’ | base64
    • The output will be as follows:
      QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOQ05WMzUzV1MmU2lnbmF0dXJlPVBjcEg5OW5zekcyJTJGdjg1ejRJYmd3Z1ZEeXdjJTNEJkV4cGlyZXM9MTYxMzgxNzAzNQ==
    At this point, users accessing the URL above with full query string parameters will be able to download the encrypted file until it expires in 1 day. If the URL above expires, the dataset will need to be updated with new query parameters
    NOTE
    If you access the URL without the string following '?', you will get a 403 forbidden.
    Hence, treat the query parameters as access credentials.

Perform the following steps to create an input dataset:

  1. Click the Datasets menu item in the CCM UI left navigation bar and click the CREATE DATASET button to create a new dataset. dataset-landing-screen.png
    Figure 3: Create a New Dataset
  2. In the Create new dataset form, enter the following details:
    • Name – Enter the name of the dataset. For example: Conditions Data.
    • Description (optional) – Enter the description of the dataset. For example: Patients with associated conditions.
    • Labels (optional) – Attach one or more key-value labels to the dataset. For example: Key: Location and Value: East US.
    • Group – Select the required group name from the drop down menu to associate this dataset with that group.
    • Location – The AWS S3 URL where data can be accessed. For example: https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc.
    • Long Description (optional) – Enter the content in GitHub-flavoured Markdown file format. You can also use the Fetch Long Description button to get the Markdown file content from an external URL.
      Fetch Long Description Dialog Box.png
      Figure 4: Fetch Long Description Dialog Box
      The following is the sample long description in Markdown format:
      - Strikethrough Text
      ~~It is Strikethrough test..~~

      - Blockquote Text
      > Test Blockquote.

      - Bold
      **A sample Description.**

      - Italic
      *It is Italics*

      - Bold and Italic
      ***Bold and Italics text***

      - Link
      This is [Website](https://www.fortanix.com/)?
    • Credentials – The credentials needed to access the data. The credentials must be in the correct JSON format and consist of:
      • Query parameters that were base64 encoded.
      • The key that was used to encrypt the file.
      {
      "query_string": "<my-query-string>",
      "encryption": {
      "key": "<my-key>"
      }
      }
      For example:
      {
      "query_string": "QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOQ05WMzUzV1MmU2lnbmF0dXJlPVBjcEg5OW5zekcyJTJGdjg1ejRJYmd3Z1ZEeXdjJTNEJkV4cGlyZXM9MTYxMzgxNzAzNQ==",
      "encryption": {
      "key": "63F0E4C07666126226D795027862ACC5848E939881C3CFE8CB3EB47DD7B3D24A"
      }
      }
      TIP
      Before saving the dataset, it is a good idea to verify that the JSON is correct. After saving the dataset you will not be able to view the credentials and access to data may fail. Any online JSON formatting tool can be used to validate that the JSON is correct.
      NOTE
      • The credentials are only passed as text when creating the dataset over an HTTPS connection.
      • It is then stored in a KMS (Fortanix Data Security Manager) and only accessible to approved enclaves.
      • Not even the Data Owner can retrieve the credentials.
    create-dataset-updated-field.png
    Figure 5: Create Input Dataset
  3. Click the CREATE DATASET button to create the input dataset.

2.3.2 Output User (Data Receiver)

Once the data has been received by the Enclave, the user application will run within the Enclave and generate output data (processed data). This data should be encrypted (using your key) before being uploaded to an untrusted store. This is achieved by defining an output dataset to be used by the workflow.

Perform the following steps:

  1. Run the following command to generate an encryption key:
    xxd -u -l 32 -p /dev/random | tr -d '\n' > ./key_out.hex
  2. Use the presign.py script included in the tar file. Run the following command to generate a presign URL for the upload:
    ./presign.py default upload <s3-directory> conditions_output.csv.enc 86400
    Where s3-directory is the directory of your S3 bucket directory.
    As an example, the output of the above command will be as shown below
    https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc?AWSAccessKeyId=<key>Signature=HFvhxaiKY0cGR9XqgGLp5zcAWac%3D&Expires=1613817880
    The output consists of two parts:
    • The location: https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc
    • Query parameters: AWSAccessKeyId=<key>Signature=HFvhxaiKY0cGR9XqgGLp5zcAWac%3D&Expires=1613817880
      The query parameters must be base64 encoded for the dataset definition using the following command:
      echo -n ‘AWSAccessKeyId=AKIAXO5N6GGNNMRXVKPA&Signature=HFvhxaiKY0cGR9XqgGLp5zcAWac%3D&Expires=1613817880’ | base64
    • The output will be as follows:
      QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOTk1SWFZLUEEmU2lnbmF0dXJlPUhGdmh4YWlLWTBjR1I5WHFnR0xwNXpjQVdhYyUzRCZFeHBpcmVzPTE2MTM4MTc4ODA=
  3. Create an output dataset with the following sample values:
    • Name – Enter the name of the dataset. For example: Conditions processing output.
    • Description (optional) – Enter the description of the dataset. For example: Patients with associated conditions.
    • Labels (optional) – Attach one or more key-value labels to the dataset. For example: Key: Location and Value: East US.
    • Group - Select the required group name from the drop down menu to associate this dataset with that group.
    • Location – The URL where data can be accessed. For example: https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc
    • Long Description (optional) – Enter the content in GitHub-flavoured Markdown file format. You can also use the Fetch Long Description button to get the Markdown file content from an external URL.
      Fetch Long Description Dialog Box.png
      Figure 6: Fetch Long Description Dialog Box
      The following is the sample long description in Markdown format:
      - Strikethrough Text
      ~~It is Strikethrough test..~~

      - Blockquote Text
      > Test Blockquote.

      - Bold
      **A sample Description.**

      - Italic
      *It is Italics*

      - Bold and Italic
      ***Bold and Italics text***

      - Link
      This is [Website](https://www.fortanix.com/)?
    • Credentials – The credentials needed to access the data. The credentials must be in the correct JSON format and consist of:
      • Query parameters that were base64 encoded.
      • The key that was used to encrypt the file.
      {
      "query_string": "<my-query-string>",
      "encryption": {
      "key": "<my-key>"
      }
      }
      For example:
      {
      "query_string": "QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOTk1SWFZLUEEmU2lnbmF0dXJlPUhGdmh4YWlLWTBjR1I5WHFnR0xwNXpjQVdhYyUzRCZFeHBpcmVzPTE2MTM4MTc4ODA=",
      "encryption": {
      "key": "63F0E4C07666126226D795027862ACC5848E939881C3CFE8CB3EB47DD7B3D24A"
      }
      }
      TIP
      Before saving the dataset, it is a good idea to verify that the JSON is correct. After saving the dataset you will not be able to view the credentials and access to data may fail. Any online JSON formatting tool can be used to validate that the JSON is correct.
      NOTE
      • The credentials are only passed as text when creating the dataset over an HTTPS connection.
      • It is then stored in a KMS (Fortanix Data Security Manager) and only accessible to approved enclaves.
      • Not even the Data Owner can retrieve the credentials.

create-dataset-updated-field.png
Figure 7: Create Output Dataset

2.4 Enclave OS Application

2.4.1 Create a General Purpose Python Docker Image

Create a docker image that will run arbitrary protected python code. The following files are used. These files are included in the tar file provided with this example:

Dockerfile:

FROM python:3.6

RUN apt-get update && apt-get install -y python3-cryptography python3-requests python3-pandas
RUN mkdir -p /opt/fortanix/enclave-os/app-config/rw
RUN mkdir -p /demo/code /demo/input

COPY ./start.py ./utils.py ./describe.py /demo/

CMD ["/demo/start.py"]

start.py: This file is the main entry point into the application.

!/usr/bin/python3
  
import os
import utils
import hashlib
from subprocess import PIPE, run

def main():
    input_folder="/opt/fortanix/enclave-os/app-config/rw"
    
    command = ["/usr/bin/python3", "/demo/describe.py"]
    
    # This downloads and decrypts all input data. File names are the object names from app config.
    for i in utils.read_json_datasets("input"):
        decrypted = utils.get_dataset(i)
        open(input_folder + i.name, 'wb').write(decrypted)
        
        # Add the file as input argument for our script
        command.append(input_folder + i.name)
        
    print("Running script")
    result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    
    # For simplicity uploading just stdout/stderr/returncode.
    utils.upload_result("output", result.returncode, result.stdout, result.stderr)
    
    print("Execution complete")

if __name__ == "__main__": main()

utils.py: This file contains the set of library functions.

!/usr/bin/env python3

import os
import sys
import string
import json
import requests
import base64
import hashlib
from subprocess import PIPE, run

from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.ciphers import (
Cipher, algorithms, modes
)

NONCE_SIZE=12
TAG_SIZE=16
KEY_SIZE=32

PORTS_PATH="/opt/fortanix/enclave-os/app-config/rw/"

def convert_key(key_hex):
key=key_hex.rstrip();
if len(key) != 64:
raise Exception("Key file must be 64 bytes hex string for AES-256-GCM")

if not all(c in string.hexdigits for c in key) or len(key) != 64:
raise Exception("Key must be a 64 character hex stream")

return bytes.fromhex(key)

def encrypt_buffer(key, data):
nonce=os.urandom(NONCE_SIZE)

cipher = Cipher(algorithms.AES(key), modes.GCM(nonce), backend=default_backend())
encryptor = cipher.encryptor()

return nonce + encryptor.update(data.encode()) + encryptor.finalize() + encryptor.tag

def decrypt_buffer(key, data):
tag=data[-TAG_SIZE:]
nonce=data[:NONCE_SIZE]

cipher = Cipher(algorithms.AES(key), modes.GCM(nonce, tag), backend=default_backend())
decryptor = cipher.decryptor()

return decryptor.update(data[NONCE_SIZE:len(data)-TAG_SIZE]) + decryptor.finalize()

class JsonDataset:
def __init__(self, location, credentials, name):
self.location = location
self.credentials = credentials
self.name = name

def read_json_datasets(port):
ports=[]
for folder in os.listdir(PORTS_PATH + port):
subfolder=PORTS_PATH + port + "/" + folder
if os.path.exists(subfolder + "/dataset"):
credentials=json.load(open(subfolder + "/dataset/credentials.bin", "r"))
location=open(subfolder + "/dataset/location.txt", "r").read()
ports.append(JsonDataset(location, credentials, folder))

return ports

def get_dataset(dataset):
url = dataset.location + "?" + base64.b64decode(dataset.credentials["query_string"]).decode('ascii')
r = requests.get(url, allow_redirects=True)
r.raise_for_status()

print("Retrieved dataset from location: " + dataset.location)
key = convert_key(dataset.credentials["encryption"]["key"])
return decrypt_buffer(key, r.content)

class RunResult:
def __init__(self, returncode, stdout, stderr):
self.returncode = returncode
self.stdout = base64.b64encode(stdout.encode()).decode('ascii')
self.stderr = base64.b64encode(stderr.encode()).decode('ascii')

def upload_result(port, returncode, stdout, stderr):
result=RunResult(returncode, stdout, stderr)
json_str=json.dumps(result.__dict__)

for dataset in read_json_datasets(port):
url = dataset.location + "?" + base64.b64decode(dataset.credentials["query_string"]).decode('ascii')
key = convert_key(dataset.credentials["encryption"]["key"])

print("Writing output to location: " + dataset.location)
requests.put(url, encrypt_buffer(key, json_str))

describe.py: This file contains the custom code called by start.py.

!/usr/bin/python3
  
import pandas as pd
import sys

for i in sys.argv[1:]:
df_do = pd.read_csv(i)
print("Dataset: " + i + "\n")
print(df_do['DESCRIPTION'].describe())
print("")

A standard docker build and docker push command must be used to create your docker and push to your registry.

As an example:

docker build -t <my-registry>/simple-python-sgx .
docker push <my-registry>/simple-python-sgx

2.4.2 Create an EOS Application Using the CCM UI

This section describes how to register the docker image in CCM and convert it to an SGX format.

  1. To create an Enclave OS application using Fortanix CCM UI, click the Applications menu item, and on the Applications page click + ADD APPLICATION to create an application. application-landing-screen.png
    Figure 8: Create an EOS Application
    For more details, refer to Add Enclave OS Application.
    add-application-1.png
    add-application-2.png
    Figure 9: Create an EOS Application
    NOTE
    Specify the key path as /opt/fortanix/enclave-os/app-config/rw/key.pem and cert path as /opt/fortanix/enclave-os/app-config/rw/cert.pem
  2. Create an image of the application.
    add-image-1.png
    Figure 10: Create an Application Image
    For more details, refer to Create an Image for Enclave OS Applications.
  3. Fetch the domain whitelisting tasks using the Fortanix CCM Tasks page.
    pending-landing-screen-ACI.png
    Figure 11: Fetch Tasks for Pending
    approved-tab1.png
    Figure 12: Fetch Tasks for Approval
  4. Approve the tasks.
    WorkflowEx12.png
    Figure 13: Approve Domain Whitelist Task WorkflowEx13.png
    Figure 14: Approve Build Whitelist Task

2.5 Create Application Config

The Docker Image recognizes the Python script using an Application Configuration which defines the ports.

Perform the following steps to create an application configuration:

  1. Click the Applications → Configurations menu item in the Fortanix CCM UI left navigation bar.
  2. Click the ADD CONFIGURATION button to add a new configuration. add-configuration-landing-screen.png
    Figure 15: Create App Configuration
  3. In the ADD APPLICATION CONFIGURATION window, fill in the following:
    1. Image – Select the application image, such as<my-registry>/simple-python-sgx:latest, for which you want to create a configuration.
      Where, <my-registry> is the location of your docker registry.
    2. Configuration Name - Enter a name for the configuration.
    3. Group - Select the required group name from the drop down menu to associate this application with that group. 
    4. Description – Enter the description of the configuration.
    5. Ports – Specify the ports to be used in the workflow. Multiple ports can be added, depending on the required connection. For example: input, output, heartbeat, and so on.
    6. Labels – Attach one or more key-value labels to the application configuration.
    7. Configuration items – These are key-value pairs used to configuring the application.
      • For EnclaveOS applications, the Key is the path of the file that contains the Value for configuring an app. 
    NOTE
    For Enclave OS applications, Fortanix permits only files in the path /opt/fortanix/enclave-os/app-config/rw.
    ADD APP CONFIGURATION.png
    Figure 16: Save Configuration
  4. Click the SAVE CONFIGURATION button to save the configuration. SAVE BUTTON.png
    Figure 17: Configuration Saved

2.6 Create a Workflow

Perform the following steps to create a workflow:

  1. Click the Workflows menu item in the Fortanix CCM UI left navigation bar.
  2. On the Workflows page, click + WORKFLOW to create a new workflow. add-workflow-button.png
    Figure 18: Create Workflow
  3. In the CREATE NEW WORKFLOW dialog box, enter the Workflow Name, assign it to a Group, and provide a Description (optional). Click CREATE WORKFLOW to access the Workflow graph.CreateWorkflow.png
    Figure 19: Created the Workflow
  4. To add an application to the Workflow graph, drag the App icon and drop it into the graph area. Click the +APPLICATION button. In the ADD APPLICATION dialog box, the App Owner must select an existing application name and image. For example: <my-registry>/simple-python-sgx:latest from the list of available application images.
    Where, <my-registry> is the location of your registry.
    CreateWorkflow-AddApp.png
    Figure 20: Select Application and Image
  5. Click the + ADD NEW CONFIGURATION button to either add a new application configuration or select an existing one. 
    AppConfig.png
    Figure 21: Add Application Configuration
  6. Add input and output datasets to the Workflow graph by dragging the dataset icon and placing it in the graph area. Click the + DATASET button. In the ADD DATASET dialog box, select from an existing dataset created in the previous section. Assign the dataset to a group using SELECT A GROUP option. AddDataset.png
    Figure 22: Add Input Dataset to Workflow
  7. Establish connections between the applications and input/output datasets. To do this, connect the Input Dataset to the Application by selecting the "Input" Target Port. Repeat this process to connect the Application to the Output Dataset with the "Output" Target Port.
    SelectPort.png
    Figure 23: Create Connection
  8. After the Workflow is complete, click the REQUEST APPROVAL button to initiate the approval process for the Workflow. 
    WorkflowApproval.png
    Figure 24: Request Workflow Approval
    WARNING
    When a draft Workflow is submitted for approval, it will be removed from the drafts list and editing it directly will no longer be possible once it is in a "pending" or "approved" state.
  9. The workflow remains in a pending state until it receives approval from all users. In the Pending menu item, click SHOW APPROVAL REQUEST to approve a Workflow. 
    WorkflowApprovalPending.png
    Figure 25: Workflow Pending Approval
  10. In thAPPROVAL REQUEST - CREATE WORKFLOW dialog box, you can either APPROVE or DECLINE a workflow. 
    show-approval-request-dialog-box.png
    Figure 26: Approve the Workflow
    NOTE
    • A user can also approve/decline a Workflow from the CCM Tasks tab.
    • Notice that the users who have approved the workflow have a green tick WorkflowEx27.png against their icon.
  11. All the users of a Workflow must approve the Workflow to finalize it. If a user declines a Workflow, the Workflow is rejected. When all the users approve the Workflow, it is deployed.
    1. CCM configures apps to access the datasets.
    2. CCM creates the Workflow Application Configs.
    3. CCM returns the list of hashes needed to start the apps.

2.7 Run the Application

After a Workflow is approved by all the users, the Applications will have the Workflow Application Configurations provided to them. This configuration has information such as which Datasets or Apps they are connected to, any user-provided files or values to be provided within an enclave, and so on.

We provide a configuration to applications using an identifier passed as an input argument.

This identifier is a sha256sum of items that you need to secure from the configuration and workflow. 

Fortanix CCM will also embed this identifier inside the certificates it issues so that it is clear what configuration is used for the KMS to allow access to credentials.

It embeds this inside a subject alternate name:

<identifier>.<mrenclave>.id.fortanix.cloud

With the identifier above, the KMS that stores the dataset credentials will authenticate and give credentials only to applications that present a proper certificate. When the application starts, CCM will keep track of which applications are allowed to access which configurations using the identifier.

To view the Application Identifier:

  1. Click the application in the approved Workflow graph. RunApp.png
    Figure 27: Workflow Application Detailed View
  2. In the detailed view of the Workflow application, copy the value of Runtime configuration hash. This ID is used to run the application. EOSExample23b1.png
    Figure 28: Copy the App Identifier
  3. To run the application, you need a node agent registered on Fortanix CCM. Depending on the node agent attestation type, run the application image using the following command:
    docker run --privileged -d -v /var/run/aesmd:/var/run/aesmd --volume /dev:/dev -p 8085:8080 --net="host" --env NODE_AGENT_BASE_URL="http://127.0.0.1:9092/v1" --env APPCONFIG_ID="f4567f41f70e8b4001078ecia8d130f62e7e45bda258f4a2b8d9a123a751fca27" <my-registry>/simple-python-sgx:converted
    Where,
    • <my-registry> is the location of where the converted application was uploaded.
    • APPCONFIG_ID is the Application Identifier.
     Output:
    WARNING: Published ports are discarded when using host network mode
    Fortanix(R) EnclaveOS Runtime Encryption Platform 2.11.276-EM
    Copyright 2017-2019 Fortanix, Inc. All rights reserved.
    Retrieved dataset from location: https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc
    Running script
    Writing output to location: https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc
    Execution complete
  4. When the App Owner starts the application with the application config identifier:
    The Data Output Owner can view the output using the following steps:
    1. Download the output file:
      aws s3 --profile download cp s3:<s3-directory>/conditions_output.csv.enc .
      For example:
      aws s3 --profile download cp s3://fortanix-pocs-data/conditions_output.csv.enc .
    2. Decrypt the file:
      Use the aes_256_gcm.py script provided in the tar file.
      ./aes_256_gcm.py dec -K ./key_out.hex -in ./conditions_output.csv.enc -out ./output.txt
      $ cat output.txt | jq .
      {
      "returncode": 0,
      "stdout": "RGF0YXNldDogL29wdC9mb3J0YW5peC9lbmNsYXZlLW9zL2FwcC1jb25maWcvaW5wdXQvY3hoY2Z4ZHZsCgpjb3VudCAgICAgICAgICAgICAgICAgICAgICAgICAgIDgzNzYKdW5pcXVlICAgICAgICAgICAgICAgICAgICAgICAgICAgMTI5CnRvcCAgICAgICBWaXJhbCBzaW51c2l0aXMgKGRpc29yZGVyKQpmcmVxICAgICAgICAgICAgICAgICAgICAgICAgICAgIDEyNDgKTmFtZTogREVTQ1JJUFRJT04sIGR0eXBlOiBvYmplY3QKCg==",
      "stderr": ""
      }
    3. The file now has the expected output:
      $ cat output.txt | jq -r .stdout | base64 -d
      Dataset: /opt/fortanix/enclave-os/app-config/input/cxhcfxdvl

      count 8376
      unique 129
      top Viral sinusitis (disorder)
      freq 1248
      Name: DESCRIPTION, dtype: object

    When the App Owner starts the application with the application config identifier:

    1. Applications will request an attestation certificate from the NodeAgent with the identifier as part of the report data.
    2. The application requests an application certificate from the NodeAgent.
    3. The Fortanix CCM verifies that the application is allowed to access the configuration.
    4. The application requests from Fortanix CCM its configuration by providing its certificate provisioned above as an authentication mechanism.
    5. The CCM does certificate authentication, extracts the application identifier from the certificate, and sends back the configuration corresponding to that identifier.
    6. The application verifies and applies the configuration hash.
    7. The application gets the credentials from URLs in the config.
    8. The application authenticates and reads/writes data from the datasets.

Comments

Please sign in to leave a comment.

Was this article helpful?
0 out of 0 found this helpful