ultravioletrs · WashingtonKK · Aug 6, 2025 · Aug 6, 2025 · Aug 6, 2025 · Aug 6, 2025
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@@ -0,0 +1,179 @@
+# Use cases
+
+This document presents a **real-world scenario** where Prism is used to perform a **secure multiparty computation (MPC)** for collaborative training of a COVID-19 prediction model. The use case demonstrates how **confidential data from multiple institutions** can be aggregated and processed without compromising data privacy.
+
+While this example is grounded in pandemic response, the underlying approach generalizes to any sensitive, collaborative AI training effort — including applications in **healthcare, finance, and government**.
+
+---
+
+## 🌐 High-Level Use Case Context
+
+Modern machine learning often requires diverse and high-quality datasets, but privacy, legal, and competitive concerns restrict data sharing. Prism addresses this by enabling **cross-organization AI training using secure enclaves** (Confidential Virtual Machines or CVMs), allowing data to remain private even during processing.
+
+This COVID-19 use case highlights:
+
+- **Collaborative AI training** without exposing raw datasets.
+- **Compliance with data protection regulations** (e.g., HIPAA, GDPR).
+- **Deployment of algorithms in secure compute environments**, fully auditable and cryptographically verified.
+
+It builds on the COVID-19 training algorithm provided in the [AI repository](https://github.com/ultravioletrs/ai/tree/main/covid19) and executes it securely through Prism and the Cocos framework.
+
+---
+
+## Summary Workflow
+
+1. **Provisioning**: Define a computation and onboard stakeholders.
+2. **Asset Registration**: Upload datasets and algorithms securely.
+3. **CVM Creation**: Set up confidential compute infrastructure.
+4. **Execution**: Trigger and monitor the multiparty computation.
+5. **Result Consumption**: Authorize and download final outputs.
+
+---
+
+## 1. Provisioning the Prism Environment
+
+### Create the Workspace and Computation
+
+- Set up a **workspace** as a secure collaboration boundary.
+- Define a **computation** to encapsulate the training task.
+- Ensure a valid **billing account** is attached for tracking usage.
+
+📘 Refer to the [Getting Started Guide](/docs/getting-started.md) for setup instructions.
+
+![New Computation](img/usecase/new_comp.png)
+
+### Invite Participating Organizations
+
+All participating institutions must:
+
+- Be registered on Prism.
+- Accept invitations to the workspace.
+- Be assigned appropriate roles (e.g., **Asset Provider**, **Result Consumer**).
+
+Each role enforces strict access control policies.
+
+![View Computation](img/usecase/view_comp.png)
+
+---
+
+## 2. Registering Assets
+
+### Uploading Datasets
+
+Datasets must follow [AI repo guidelines](https://github.com/ultravioletrs/ai/tree/main/covid19#testing-with-cocos). Each dataset should:
+
+- Be zipped and verified via checksum.
+- Be uploaded using the CLI and linked to the computation.
+
+Example:
+
+```bash
+./build/cocos-cli data ../ai/covid19/datasets/h1.zip ./private.pem
+```
+
+Repeat for all datasets.
+
+![COVID-19 Assets](img/usecase/datasets.png)
+
+### Uploading the Algorithm
+
+Python-based training scripts should be uploaded with their dependencies:
+
+```bash
+./build/cocos-cli algo ../ai/covid19/train.py ./private.pem \
+  -a python \
+  -r ../ai/covid19/requirements.txt
+```
+
+![Upload Algorithm](img/usecase/upload_algo.png)
+
+The algorithm must conform to the Cocos runtime interface (as detailed in the AI repo).
+
+![Algorithm Received](img/usecase/algo_received.png)
+
+---
+
+## 3. Confidential Compute via CVM
+
+CVMs are secure environments where the algorithm executes under **hardware-based encryption** (Intel TDX, AMD SEV, etc.).
+
+Steps:
+
+1. Go to the **CVMs** page and click **New CVM**.
+2. Complete the required configuration.
+
+![Create CVM](img/usecase/create_cvm.png)
+
+📘 Detailed setup instructions: [CVM Docs](/docs/cvms.md)
+
+After creation, export the CVM's gRPC endpoint:
+
+```bash
+export AGENT_GRPC_URL=192.0.2.1:6110
+```
+
+This allows CLI interaction with the agent.
+
+---
+
+## 4. Execution of the Secure Computation
+
+Once all assets are in place:
+
+1. Return to the **Computations** dashboard.
+2. Click **Run** and select the target CVM.
+3. Monitor the computation state in real time.
+
+The computation will transition through:
+
+- **Waiting for Algorithm**
+- **Waiting for Datasets**
+- **Running**
+- **Completed**
+
+![Running Computation](img/usecase/running_computation.png)
+
+All events and logs are cryptographically signed and verifiable.
+
+![Computation Progress](img/usecase/computation_progress.png)
+
+---
+
+## 5. Result Retrieval and Post-Processing
+
+Authorized users can retrieve results via:
+
+```bash
+./build/cocos-cli result ./private.pem
+```
+
+![Completed Computation](img/usecase/completed_computation.png)
+
+Note: Results are **one-time consumable** and linked to access control lists defined by the computation owner.
+
+![Downloaded results](img/usecase/consumed_results.png)
+
+---
+
+## 🔍 Broader Real-World Applications
+
+This COVID-19 training use case serves as a **template for broader domains**:
+
+| Domain                                                                       | Use Case Example                                                                        |
+| ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| **[Healthcare](https://github.com/ultravioletrs/ai/tree/main/covid19)**      | Secure joint training of diagnostic models across hospitals.                            |
+| **[Finance](https://github.com/ultravioletrs/ai/tree/main/fraud-detection)** | Fraud detection models using data from multiple banks.                                  |
+| **Insurance**                                                                | Risk modeling across industry consortia.                                                |
+| **[Energy](https://github.com/ultravioletrs/ai/tree/main/rul-turbofan)**     | Failure prediction and remaining useful life in turbo engines based on historical data. |
+| **Public Sector**                                                            | Secure census or mobility analysis across government bodies.                            |
+
+Prism, powered by [Cocos](https://docs.cocos.ultraviolet.rs), makes **confidential AI collaboration** practical at scale, with cryptographic audit trails and privacy guarantees.
+
+---
+
+## 🔗 References
+
+- [AI COVID-19 Training Repo](https://github.com/ultravioletrs/ai/tree/main/covid19)
+- [Cocos CLI Docs](https://docs.cocos.ultraviolet.rs/cli)
+- [Prism Getting Started Guide](/docs/getting-started.md)
+- [CVM Reference Manual](/docs/cvms.md)
diff --git a/docusaurus.config.ts b/docusaurus.config.ts
@@ -44,7 +44,7 @@ const config: Config = {
                     // Please change this to your repo.
                     // Remove this to remove the "edit this page" links.
                     editUrl:
-                        'https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/',
+                        'https://github.com/ultravioletrs/prism-docs/tree/main/',
                 },
                 blog: false,
                 theme: {

diff --git a/sidebars.ts b/sidebars.ts
@@ -86,7 +86,8 @@ const sidebars: SidebarsConfig = {
                 'logs_api_docs'
             ]
         },
-        "billing"
+        "billing",
+        "use_case"
     ],
 };