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@@ -24,7 +24,7 @@ Remote Data Science - Code for `computing on data`, you `do not own` and `cannot
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💻 `hagrid quickstart`
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- In the tutorial you will learn how to install and deploy:
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`PySyft` = our `torch`-like 🐍 Python Library
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`PySyft` = our `numpy`-like 🐍 Python Library
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`PyGrid` = our 🐳 `docker` / `k8s` Data Platform
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- During quickstart we will deploy `PyGrid` to localhost with 🐳 `docker`, however 🛵 HAGrid can deploy to `k8s` or a 🐧 `ubuntu` VM on `azure` / `gcp` / `ANY_IP_ADDRESS` by using 🔨 `ansible`†
@@ -36,9 +36,9 @@ Remote Data Science - Code for `computing on data`, you `do not own` and `cannot
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✅ `Linux` ✅ `macOS`\* ✅ `Windows`†‡
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-`HAGrid` = our our handy 🛵 cli tool
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-`PySyft` = our `torch`-like 🐍 Python Library
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-`PyGrid` = our 🐳 `docker` / `k8s` Data Platform
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-`PySyft` = our `numpy`-like 🐍 Python library for computing on `private data` in someone else's `Domain` server
`Syft` is OpenMined's `open source` stack that provides `secure` and `private` Data Science in Python. Syft decouples `private data` from model training, using techniques like [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [Encrypted Computation](https://en.wikipedia.org/wiki/Homomorphic_encryption). This is done with a `torch`-like interface and integration with `Deep Learning` frameworks so that you as a `Data Scientist` can maintain your current workflow while using these new `privacy-enhancing techniques`.
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`Syft` is OpenMined's `open source` stack that provides `secure` and `private` Data Science in Python. Syft decouples `private data` from model training, using techniques like [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [Encrypted Computation](https://en.wikipedia.org/wiki/Homomorphic_encryption). This is done with a `numpy`-like interface and integration with `Deep Learning` frameworks, so that you as a `Data Scientist` can maintain your current workflow while using these new `privacy-enhancing techniques`.
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### Why should I use Syft?
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`Syft` allows a `Data Scientist` to ask `questions` about a `dataset` and, within `privacy limits` set by the `data owner`, get `answers` to those `questions`, all without obtaining a `copy` of the data itself. We call this process `Remote Data Science`. It means in a wide variety of `domains` across society, the current `risks` of sharing information (`copying` data) with someone such as, privacy invasion, IP theft and blackmail will no longer prevent the ability to utilize the vast `benefits` such as innovation, insights and scientific discovery.
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`Syft` allows a `Data Scientist` to ask `questions` about a `dataset` and, within `privacy limits` set by the `data owner`, get `answers` to those `questions`, all without obtaining a `copy` of the data itself. We call this process `Remote Data Science`. It means in a wide variety of `domains` across society, the current `risks` of sharing information (`copying` data) with someone such as, privacy invasion, IP theft and blackmail will no longer prevent the vast `benefits` such as innovation, insights and scientific discovery which secure access will provide.
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No more cold calls to get `access` to a dataset. No more weeks of `wait times` to get a `result` on your `query`. It also means `1000x more data` in every domain. PySyft opens the doors to a streamlined Data Scientist `workflow`, all with the individual's `privacy` at its heart.
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@@ -222,7 +223,7 @@ Provides services to a group of `Data Owners` and `Data Scientists`, such as dat
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🎥 <ahref="https://www.youtube.com/watch?v=Pr4erdusiW0">Privacy-Preserving Data Science - TWiML Talk #241</a><br />
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🎥 <ahref="https://www.youtube.com/watch?v=NJBBE_SN90A">Privacy Preserving AI - PyTorch Devcon 2019</a><br />
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📖 <ahref="https://arxiv.org/pdf/2110.01315.pdf">Towards general-purpose infrastructure for protecting ...</a><br />
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📖 <ahref="https://arxiv.org/pdf/2104.12385.pdf">Syft 0.5: A platform for universally deployable structured ...</a><br />
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📖 <ahref="https://arxiv.org/pdf/2104.12385.pdf">Syft 0.5: A platform for universally deployable ...</a><br />
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📖 <ahref="https://arxiv.org/pdf/1811.04017.pdf">A generic framework for privacy preserving deep learning</a>
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</sup></sup></p>
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</div>
@@ -272,8 +273,39 @@ OpenMined and Syft appreciates all contributors, if you would like to fix a bug
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