In early 2021, Consequential, alongside the Open Data Institute (ODI), conducted a short, qualitative research project to explore the impact of data ethics tools in supporting a vision of a world where tech and data works for everyone.
Protect privacy of your data by encrypting it. Outsource computations on the encrypted data, and decrypt at your end to view results.
In this code tutorial, I show the difference between local and global sensitivity, and program from scratch how to calculate local sensitivity for both the bounded and unbounded definitions of differential privacy.
More on Structured Transparency: Input and Output Verification, and Flow Governance
Speakers: Kritika Prakash , Lucile Saulnier, Dmitrii Usynin, Zarreen Naowal Reza This talk provides an example of private deep learning u
In this code tutorial, I show how to implement empirically the calculation of the global sensitivity for the bounded and unbounded definitions of differential privacy.
Interview with Thiago Costa Porto, OpenMined's Featured Contributor for May 2021!
An attempt to avoid talking past each other so we can instead direct our efforts towards moving the needle on privacy through privacy innovation
Steganography is the art of hiding information. In this blog post we’ll see how neural networks can be used as a vehicle for covert communication.
An interview with Yuxin Wang, a rising star in programming analysis of differential privacy.
This post introduces the concept of attestation with Intel SGX enclaves
In this code tutorial, we implement differential identifiability, a differential privacy definition produced by Jaewoo Lee et al. This definitions helps practitioners to decide in a more intuitive manner what the value of epsilon should be, a major problem in the field.
Interview with Fatemeh Mireshghallah, OpenMined's Featured Contributor for April 2021!
This post is a first introduction to the basic principles of Confidential Computing.
Introduction Install PySyft Install PyTorch v1.8.0 (1 min) Install torchvision v0.9.0 (11 min) Install aiortc (7 min) Install syft 0.5.0rc1
The authors of the paper (Jaewoo Lee et al.) behind this code tutorial proposed bounds for epsilon so that its value may not yield a random output query result that leads to a posterior that is greater than the disclosure risk. In this post, we code their solution.
This is the summary of the talk “Building differential privacy at scale” by Miguel Guevara ( Product Manager at Google) at the OpenMined Pr
This is a summary of the talk by Daniel Escudero at the OpenMined Privacy Conference 2020. Daniel Escudero talked about PRIMAL, a framewor
In non-federated settings, adaptive optimization methods have desirable convergence properties. Can federated versions of these adaptive optimizers, including Adagrad, Adam, and Yogi facilitate better convergence in the presence of heterogeneous data?
Interview with Tudor Cebere, OpenMined's Featured Contributor for March 2021!
Summary: We learn best with toy code we can play with and diving into the documentations. This beginner friendly blog post covers a quick an
Summary: Sometimes knowing the basics is important! This beginner friendly blog post covers a quick and easy introduction to Differential Pr
Differential Privacy using PyDP - An introductory tutorial. Here's an outline: What does Differential Privacy try to address? Why doesn't anonymization suffice? PyDP Example Walkthrough
We’re are very excited to announce that OpenMined was yet again selected as a mentor organization for Google Summer of Code (GSoC 2021)! Thi
