Holistic Classification of PETs

With so many options for privacy enhancing technologies (PETs), it is hard to choose which is correct for your project.  Questions abound ab

OpenMined's R3Q1 Padawan Program Graduates

Congratulations to all of our R3Q1 Padawan Program Graduates!‌ We are thrilled to recognize our Graduates of OpenMined's Padawan Progr

Padawan Program Alumni

OpenMined's Padawan Program continues to grow and support a diverse group of learners. We recognize our Padawan Program Alumni, acknowledge

OpenMined's R2Q4 Padawan Program Graduates

Congratulations to all of our R2Q4 Padawan Program Graduates!

Classifying the Challenges of Privacy Enhancing Technologies (PETs) in IoT Data Markets.

The rise of Privacy Enhancing Technologies (PETs) has witnessed tremendous growth in recent years. A recent research publication from MIT [1

Understanding the role of Privacy Enhancing Technologies (PETs) in the IoT Data Market.

The data generated by the Internet of Things (IoT) is leading a positive transformation across industries, not only in traditional ones such

Yes, privacy is worth the effort. Here’s why

When we advocate for privacy, we tend to concentrate on the negative consequences of privacy violations [56; 32; 19; 50]. These portrayals a

Local sensitivity for differential privacy from scratch

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.

Global sensitivity for differential privacy from scratch

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.

Confidential Computing Explained. Part 2 : Attestation

This post introduces the concept of attestation with Intel SGX enclaves

Differential Identifiability

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.

Confidential computing explained. Part 1: introduction

This post is a first introduction to the basic principles of Confidential Computing.

Choosing Epsilon for Differential Privacy

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.

AI Privacy and Compliance

Privacy is becoming increasingly valuable. As more individuals learn of the risks of data breaches, their own vulnerability to AI monitoring, and their rights to protect their privacy, technology needs must be more compliant than ever.

Scrambling Memorized Info in GPT-2

What do GPT-2 and GPT-3 know about us?

Society Runs on Information Flows

Information flows form the basis society. How can we understand them better? Why are they often broken and how can we possibly fix them?

Duet Demo - How to do data science on data owned by a different organization

Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi

The Lightness of Being Forgotten

The use of my private information has long bothered me. However, I have not done much about the right to be forgotten.

How to Protect Your Privacy Online (right now)?

Since you clicked open the link to this post, may I be so bold as to assume that you, my dear reader, are as concerned about internet privac