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docs/conf.py

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# coding=utf-8
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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

examples/distributed_noise_generation.py

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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

examples/dp_logistic_regression.py

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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

examples/dp_sgd_flax_linen_mnist.ipynb

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"source": [
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"# DP-SGD tutorial using Flax Linen on MNIST\n",
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"\n",
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"**Copyright 2026 DeepMind Technologies Limited.**\n",
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"**Copyright 2025 DeepMind Technologies Limited.**\n",
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"\n",
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"Welcome to Jax Privacy for Flax Linen! In this tutorial you will learn how to train a simple CNN model in a differentially-private (DP) way using DP-SGD algorithm. We will train our model on the MNIST dataset.\n",
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"\n",

examples/dp_sgd_keras_gemma3_lora_finetuning_samsum.ipynb

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"source": [
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"# Tutorial of DP-SGD LoRA fine-tuning Gemma3 in Keras on SAMSum dataset\n",
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"\n",
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"**Copyright 2026 DeepMind Technologies Limited.**\n",
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"**Copyright 2025 DeepMind Technologies Limited.**\n",
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"\n",
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"Welcome to Jax Privacy for Keras! In this tutorial you will learn how to LoRA fine-tune [Gemma3 LLM](https://www.kaggle.com/models/keras/gemma3) in a differentially private (DP) way using [DP-SGD algorithm](https://medium.com/pytorch/differential-privacy-series-part-1-dp-sgd-algorithm-explained-12512c3959a3). We will fine-tune the model on the [SAMSum dataset](https://huggingface.co/datasets/Samsung/samsum).\n",
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examples/dp_sgd_keras_gemma3_synthetic_data.ipynb

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"source": [
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"# Tutorial: Generating Differentially Private Synthetic Data\n",
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"\n",
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"**Copyright 2026 DeepMind Technologies Limited.**\n",
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"**Copyright 2025 DeepMind Technologies Limited.**\n",
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"\n",
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"Before diving into this notebook, we highly recommend familiarizing yourself with the [\"Tutorial of DP-SGD LoRA fine-tuning Gemma3 in Keras on SAMSum dataset\"](https://github.com/google-deepmind/jax_privacy/blob/4aa89276aad9d453a9dfb872a672a82396f3a8b7/examples/dp_sgd_keras_gemma3_lora_finetuning_samsum.ipynb) (referred to as the \"DP-SGD LoRA tutorial\" from now on). This tutorial builds upon that previous work, and therefore, code concepts introduced there will not be explained in detail here.\n",
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examples/dpmf_strategy_optimization.py

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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

examples/jax_api_example.py

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# coding=utf-8
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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

examples/keras_api_example.py

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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

jax_privacy/__init__.py

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# Copyright 2026 DeepMind Technologies Limited.
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# Copyright 2025 DeepMind Technologies Limited.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.

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