Tim Grube

name

Tim Grube

position

Research Assistant / Doctoral Researcher

Smart Protection in Infrastructures and Networks (SPIN) (former Secure Smart Infrastructures SSI) Group 

GRK 2050 - Privacy and Trust for Mobile Users

Software Campus Project KomBi

email

grube(at)tk(dot)tu-darmstadt(dot)de

grube(at)cs(dot)tu-darmstadt(dot)de

phone

+49 (6151) 16 - 23205

fax

+49 (6151) 16 - 23202

office

S2-02 A 316

postal address

TU Darmstadt - FB 20

FG Telekooperation
Hochschulstraße 10
D-64289 Darmstadt
Germany

 

Short Biography

Tim is a doctoral researcher in the Smart Protection in Infrastructures and Networks (SPIN) group at the Technische Universität Darmstadt. In 2015, he also joined the graduate college Privacy and Trust for Mobile Users (PAT). He works on a PhD thesis in the area of efficient and anonymous communication, besides that he is interested in graph theory, visualization of dynamic systems and P2P systems. In addition to his work in the graduate college, Tim is leading the Software Campus project Complexity Reduction in Big Data (KomBi), researching in the field of graph-based data visualization. 

Tim received his master degree in Computer Science with a focus on distributed systems from the Technische Universität Darmstadt in 2014. His thesis dealt with complexity reduction in large networks by using sampling techniques. 

Research Interests

  • Distributed Communication

    • Privacy
    • Efficiency
    • Topologies for privacy preserving and efficient communication

  • Dynamic Systems

    • Graphs
    • Graph Theory
    • P2P Systems
    • Visualisation of Dynamic Systems

Theses & Teaching

Supervised Theses (ongoing and finished)

  • Advanced Ants to establish Anonymous Communication Overlays
  • Measuring a User's Privacy
  • Source Protection in P2P-based Pub/Sub-Networks
  • What is the Influence of Topologies on Achievable Privacy?
  • Reducing Data Complexity while Maintaining its Meaning
  • Survey and Comparison: Complexity Reduction in Graphs to support the Visualization of Big Data
  • Framework for Visualization of Massive Data
  • Visualization of Large Dynamic Networks
  • Analysis of a Street Network with Dynamic Graphs
  • Connecting multiple uMundo Workspaces
  • Evaluation of Codecs for HD Video Streaming in Wireless Home Networks

Please contact me if you are interested in writing a Bachelor or Master thesis. Please provide a short topic proposal and express your motivation for this topic in a few sentences. I am regularly supervising theses. 

 Teaching

Winter Term 2017/18

  • TK1 - Exercise
  • Ubiquitous Computing in Geschäftsprozessen

Winter Term 2016/17

  • TK1 - Exercise 
  • TK Seminar

    • Differential Privacy in Practice
    • A Survey of Trends in Privacy Measurements

Summer Term 2015

  • TK Seminar

    • Complexity Reduction in Graphs
    • Understanding Freenet
    • Using Monotonicity to Predict Sampling Steps

Winter Term 2014/15

  • TK Seminar

    • Attacks on Anonymous Communication
    • Measures of Anonymity in Communication Networks
    • Multi-Layer Resilience in Computer Networks

Publications

Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration

Author Tim Grube, Florian Volk, Max Mühlhäuser, Suhas Bhairav, Vinay Sachidananda, Yuval Elovici
Date October 2017
Kind Inproceedings
Book titleto appear at: 10th International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services
KeyTUD-CS-2017-0204
Research Areas CYSEC, privacy trust, Telecooperation, - SST - Area Smart Security and Trust, - SSI - Area Secure Smart Infrastructures, SPIN: Smart Protection in Infrastructures and Networks, Fachbereich Informatik, CRISP
Abstract Human exploration of large graph structures be- comes increasingly difficult with growing graph sizes. A visual representation of such large graphs, for example, social networks and citational networks, has to find a trade-off between showing details in a magnified view and the overall graph structure. Displaying these both aspects at the same time results in an overloaded visualization that is inaccessible for human users. In this paper, we present a new approach to address this issue by combining and extending graph-theoretic properties with com- munity detection algorithms. Our approach is semi-automated and non-destructive. The aim is to retain core properties of the graph while–at the same time–hiding less important side information from the human user. We analyze the results yielded by applying our approach to large real-world network data sets, revealing a massive reduction of displayed nodes and links. 
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