{"id":19733976,"url":"https://github.com/scaleoutsystems/tee-iot","last_synced_at":"2025-10-07T02:36:27.654Z","repository":{"id":104982523,"uuid":"491824973","full_name":"scaleoutsystems/tee-iot","owner":"scaleoutsystems","description":null,"archived":false,"fork":false,"pushed_at":"2022-08-24T13:27:08.000Z","size":12,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-01-10T18:23:33.057Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/scaleoutsystems.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-13T08:49:35.000Z","updated_at":"2023-02-24T08:59:39.000Z","dependencies_parsed_at":"2023-05-07T11:30:51.073Z","dependency_job_id":null,"html_url":"https://github.com/scaleoutsystems/tee-iot","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scaleoutsystems%2Ftee-iot","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scaleoutsystems%2Ftee-iot/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scaleoutsystems%2Ftee-iot/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scaleoutsystems%2Ftee-iot/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scaleoutsystems","download_url":"https://codeload.github.com/scaleoutsystems/tee-iot/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241059014,"owners_count":19902296,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T00:34:50.264Z","updated_at":"2025-10-07T02:36:22.602Z","avatar_url":"https://github.com/scaleoutsystems.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CASA IoT in Trusted Execution Environment\nIn this PoC we mock a use case in which reducer and combiner run on an enclave in a cloud environment, while a classic human activity recognition training task ([CASA](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+from+Continuous+Ambient+Sensor+Data)) is run on an IoT board. This PoC is intended to be used with [Intel SGX](https://www.intel.com/content/www/us/en/developer/tools/software-guard-extensions/overview.html) and it was tested using [Azure Confidential Computing](https://azure.microsoft.com/en-us/solutions/confidential-compute/).\n\n# Table of Contents\n- [CASA IoT in Trusted Execution Environment](#casa-iot-in-trusted-execution-environment)\n- [Table of Contents](#table-of-contents)\n  - [Running the PoC](#running-the-poc)\n    - [Get data and setup the environment](#get-data-and-setup-the-environment)\n    - [Deploy the PoC](#deploy-the-poc)\n    - [Running the PoC](#running-the-poc-1)\n    - [Clean up](#clean-up)\n\n## Running the PoC\n\n### Get data and setup the environment\nFirst, download the data by running:\n\n```bash\nbin/get_data.sh\n```\n\nThen, init the Python virtual environment by running:\n\n```bash\nbin/init_venv.sh\n```\n\u003e **Note** this command was tested with Python 3.8\n\nNow you can generate the seed model by running:\n\n```\nclient/entrypoint init_seed\n```\n\nThe next step is to build the compute package:\n\n```\nbin/build_package.sh\n```\n\n### Deploy the PoC\n```bash\nsudo docker-compose up -d\n```\n\u003e **Note** you may need to login into Scaleout's GitHub registry to access ghcr.io/scaleoutsystems/tee-gc/fedn:latest\n\n### Running the PoC\nThe reducer UI is now available on `https://localhost:8090`. The quickest way to upload `package.tar.gz` and `seed.npz` is via REST API.\n\n```bash\n# Upload package\ncurl -k -X POST \\\n    -F file=@package.tar.gz \\\n    -F helper=\"keras\" \\\n    https://localhost:8090/context\n\n# Upload seed\ncurl -k -X POST \\\n    -F seed=@seed.npz \\\n    https://localhost:8090/models\n```\n\nNow navigate to https://localhost:8090 and start the experiment using the UI.\n\n### Clean up\nYou can release resources by running the following: `sudo docker-compose down`.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscaleoutsystems%2Ftee-iot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscaleoutsystems%2Ftee-iot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscaleoutsystems%2Ftee-iot/lists"}