Interview with Irina Bejan, OpenMined's Featured Contributor for July 2021!
PySyft is a Python library for secure and private Deep Learning. It uses Federated Learning, Differential Privacy, and Encrypyted Computatio
When we advocate for privacy, we tend to concentrate on the negative consequences of privacy violations [56; 32; 19; 50]. These portrayals a
Interview with George Kaissis, OpenMined's Featured Contributor for June 2021!
In this interview, Chinmay Shah, who leads the Differential Privacy team at OpenMined, talks about his interests, the team's journey and the exciting road ahead!
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 PyTorch v1.8.1 (1 min) Install other dependencies (6 min) Install syft 0.5.0 (6 min) Testing the environment In
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.
Observe how a differential privacy infrastructure from the ground up. You'll see use cases that bolster the fact that making the infrastructure “user-centric” is crucial. We also discuss why people are reluctant to use such infrastructure
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?
