Lab Machine learning on encrypted data

This course is listed

• in Aachen RWTHonline as Lab Machine learning on encrypted data,
• in Bonn Basis as MA-INF 4322 - Lab Machine Learning on encrypted data.

Lecture

Michael Nüsken

Time & Place

To participate please apply by email describing why you are interested and your background.

We meet by appointment.

• Monday, 16⁰⁰-18⁰⁰, b-it 2.106 or/and digital seminar room..
• Friday, 10⁰⁰-12⁰⁰, digital seminar room..

Kick-off meeting: Monday, 24 October 2022, 16⁰⁰, b-it 2.106 or/and digital seminar room..

Schedule

• Friday, 31 March 2023, 10⁰⁰-12⁰⁰. Timon Oerder.
  Lab project final: Privacy-preserving diagnostics.
  
  based on:

  • ...

Contents

With the rise of more and more mechanisms and installations of data science methodology to automatically analyze large amounts of possibly privacy infringing data we have to carefully understand how to protect our data. Also more and more fake data shows up and we have to find ways to distinguish faked from trustable data. At the same time we want to allow insightful research and life-easing analyzes to be possible. This seeming contradiction has lead to various efforts for unifying both: protecting data and allowing analyzes, at least to some extent and possibly under some restrictions. See Munn et al. (2019) for a review on challenges and options.

The target of the lab is to understand how computations on encrypted data may work in one particular application that we are chosing together. Ideally, we can come up with a novel solution for performing an unconsidered algorithm. We study the tasks and tools, select algorithms, find a protocol, prototype an implemention, perform a security analysis, present an evaluation, ...

We will plan and distribute work shares together in our meetings.

Prerequisites

Basic knowledge of cryptography and privacy, machine learning and data science is helpful.
A fast understanding of mathematical and computer science topics is required.

Allocation

Lab.

• Master in Media Informatics: PR, 9 ECTS.
  Students have to register this course in RWTHonline.
• Master in Computer Science at University of Bonn: MA-INF 4322, 9 CP.
  Students have to register this course in BASIS.