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
Differential Privacy 3 months ago

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
Privacy 8 months ago

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
Privacy a year ago

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.
Differential Privacy a year ago

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.
Differential Privacy a year ago

Confidential Computing Explained. Part 2 : Attestation

This post introduces the concept of attestation with Intel SGX enclaves
Deep Learning a year ago

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.
Differential Privacy a year ago

Confidential computing explained. Part 1: introduction

This post is a first introduction to the basic principles of Confidential Computing.
Deep Learning a year ago

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.
Differential Privacy a year ago

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.
Privacy a year ago

Scrambling Memorized Info in GPT-2

What do GPT-2 and GPT-3 know about us?
Privacy a year ago

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?
Private AI Series a year ago

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
PriCon2020 a year ago

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.
Privacy a year ago

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
Privacy a year ago