[incubator-teaclave] 07/07: [docs] Add threat model

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mssun pushed a commit to branch master
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commit e1ad6b65cb27110774b572cda537fa503f7eb655
Author: Mingshen Sun <bo...@mssun.me>
AuthorDate: Wed Apr 22 17:12:53 2020 -0700

    [docs] Add threat model
---
 README.md            |  5 +++++
 docs/threat_model.md | 41 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 46 insertions(+)

diff --git a/README.md b/README.md
index 8536cee..bf168d0 100644
--- a/README.md
+++ b/README.md
@@ -34,6 +34,11 @@ platform, making computation on privacy-sensitive data safe and simple.
 
 - [My First Function](docs/my-first-function.md)
 
+### Design
+
+- [Threat Model](docs/threat_model.md)
+- [Mutual Attestation: Why and How](docs/mutual-attestation.md)
+
 ## Contributing
 
 Teaclave is open source in [The Apache Way](https://www.apache.org/theapacheway/),
diff --git a/docs/threat_model.md b/docs/threat_model.md
new file mode 100644
index 0000000..2d0e170
--- /dev/null
+++ b/docs/threat_model.md
@@ -0,0 +1,41 @@
+# Threat Model
+
+With its strongest security setting applied, Teaclave guarantees data
+confidentiality even if all parties along the computation path, privileged or
+not, are untrusted. This includes:
+
+- Internet service provider
+- Cloud provider
+- Function provider
+- Other data providers 
+
+Consider the following scenario. A small business needs to employ image
+classification techniques in its daily production. However, the business does
+not have the capabilities to train a high-quality machine learning model, nor
+does it have the hardware resources to host the machine learning
+infrastructures. Under such circumstances, the only solution is to subscribe to
+some cloud computing service and run the needed image classification tasks
+remotely. However, this solution requires the small business to upload its
+private data to the cloud, which may deeply concerns the business owner and
+hinders the deployment of such techniques.
+
+With Teaclave, privacy concerns above are no more. The small business can
+subscribe to the cloud service from company A, rent the machine learning model
+from company B, and use the deep learning inference engine provided by company
+C. None of these parties need to trust another, yet the computation can commence
+with everyone's privacy respected.
+
+In the settings above, the root of trust converges to Intel and its SGX-enabled
+CPU chips. Before the computation starts, Teaclave is booted as a secure SGX
+enclave on one of these CPUs owned by the cloud service provider. After that,
+each party can **remotely** attest the authenticity of the hardware and the
+integrity of Teaclave platform. Private data are securely provisioned to the
+Teaclave enclave only if the attestation passes. After the provision, no
+privileged software is able to access the memory content owned by the enclave
+from outside.
+
+The remote attestation functionality implemented by Teaclave is augmented from
+the method described by an Intel [white paper](https://arxiv.org/abs/1801.05863).
+The complicated structure of Teaclave requires additional work for remote
+attestation, which is explained in details via a separate
+[documentation](mutual_attestation.md).


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