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tensorflow
Library for training machine learning models with privacy for training data
Output exceeds the [size limit](command:workbench.action.openSettings?%5B%22notebook.output.textLineLimit%22%5D). Open the full output data[ in a text editor](command:workbench.action.openLargeOutput?26d3f78e-c615-46bf-839b-7f597f34c976) --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) c:\Users\XR\anaconda3\envs\tensorflowgpu_new2\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def) 1811 try:...
Hi, is there a particular reason that this repo is stuck with a version of numpy (1.21) that is not compatible with python 11? - According to the numpy docs...
I got a ValueError when using TensorFlow to create a model. Based on the error there is a problem that occurs with the kernel regularizer applied on the Conv2D layer...
Hello, I would like to bring to your attention that using the random number generator from TensorFlow *could* lead to vulnerabilities when sampling from a distribution to fulfill differential privacy...
# Patching CVE-2007-4559 Hi, we are security researchers from the Advanced Research Center at [Trellix](https://www.trellix.com). We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a...
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Sparsity Preserving DP-SGD in TF Privacy [2 of 4] Adds sparse noise utilities to privately select sparse indices from and add sparse noise to gradients. See https://research.google/blog/sparsity-preserving-differentially-private-training/ for more details...
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Sparsity Preserving DP-SGD in TF Privacy [3 of 4] Add contribution count function for embedding layer. See https://research.google/blog/sparsity-preserving-differentially-private-training/ for more details on the algorithm.
Sparsity Preserving DP-SGD in TF Privacy [4 of 4] Integrate sparsity preserving noise into DP Keras Model with fast gradient clipping. See https://research.google/blog/sparsity-preserving-differentially-private-training/ for more details on the algorithm.
Implementation of the paper **Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning** from Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch. https://arxiv.org/abs/2406.08039
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Library for training machine learning models with privacy for training data