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?

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

This is a summary of Duet Tutorial by Andrew Trask which was presented at OpenMined Privacy Conference 2020.Brief intro to federated learnin

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