Cape Privacy (Formerly Dropout Labs)
Privacy & Trust Management for Machine Learning. Operationalize compliance for collaborative machine learning across your organization.
Introducing Cape Core: 10 Questions with Cape Privacy’s Head of Product
Introducing Cape Core: 10 Questions with Cape Privacy’s Head of Product
Katharine Jarmul is Head of Product at Cape Privacy, a company enabling collaborative and compliant data science and machine learning by…
Cape Python: Apply Privacy-Enhancing Techniques to Protect Sensitive Data in Pandas and Spark
Cape Python: Apply Privacy-Enhancing Techniques to Protect Sensitive Data in Pandas and Spark
We’re extremely excited to have recently released the Cape Python library. This library is one of the first building blocks to make your…
Privacy-Preserving Machine Learning 2019: A Year in Review
Privacy-Preserving Machine Learning 2019: A Year in Review
Highlighting the top news, research, code, and community events that impacted PPML in 2019.
Federated Learning with Secure Aggregation in TensorFlow
Federated Learning with Secure Aggregation in TensorFlow
Integrating TF Encrypted and TensorFlow to
Introducing PySyft TensorFlow
Introducing PySyft TensorFlow
We’re excited to announce our contribution of TensorFlow support to OpenMined’s PySyft project!
Encrypted Deep Learning Training and Predictions with TF Encrypted Keras
Encrypted Deep Learning Training and Predictions with TF Encrypted Keras
We are super pleased to announce the addition of a Keras compatible API to TF Encrypted!
Bridging Microsoft SEAL into TensorFlow
Bridging Microsoft SEAL into TensorFlow
The road to machine learning with homomorphic encryption
A Path to Sub-Second, Encrypted Skin Cancer Detection
A Path to Sub-Second, Encrypted Skin Cancer Detection
How we used TF Encrypted to detect skin cancer using encrypted images in 36 seconds.
Dropout Labs wins the Confidential Computing Challenge!
Dropout Labs wins the Confidential Computing Challenge!
Dropout Labs is very excited to announce that we’ve won the Google Cloud + Intel Confidential Computing Contest!
Secure Logistic Regression: MPC vs Enclave Benchmark
Secure Logistic Regression: MPC vs Enclave Benchmark
With Ben DeCoste.
Privacy-Preserving Machine Learning 2018: A Year in Review
Privacy-Preserving Machine Learning 2018: A Year in Review
We highlight the news, research, code, communities, organizations, and economics that made 2018 the breakout year for PPML.
Announcing SecureNN in tf-encrypted
Announcing SecureNN in tf-encrypted
We are pleased to announce that our implementation of SecureNN has landed in tf-encrypted!
