About

• Since 05/2017 – Researcher / Ph.D. student at Leipzig University
• 2017 – M. Sc. Computer Science, Leipzig University
• 2014 – B. Sc. Computer Science, University of Jena

Contact

• E-Mail: franke [at] informatik.uni-leipzig.de
• Office: Room P-412, Paulinum, Augustusplatz 10, 04109, Leipzig

Consultation Hours

By appointment only.

Research

• Privacy-preserving Record Linkage (PPRL) for Big Data

Publications

Franke, Martin; Sehili, Ziad; Rohde, Florens; Rahm, Erhard Evaluation of Hardening Techniques for Privacy-Preserving Record Linkage Proceedings of the 24th International Conference on Extending Database Technology (EDBT), pp. 289-300 2021-03

Sehili, Z.; Rohde, F.; Franke, M.; Rahm, E. Multi-Party Privacy Preserving Record Linkage in Dynamic Metric Space Proc. 19. GI-Fachtagung für Datenbanksysteme für Business, Technologie und Web (BTW), 2021 2021-02

Rohde, Florens; Franke, Martin; Sehili, Ziad; Lablans, Martin; Rahm, Erhard Optimization of the Mainzelliste software for fast privacy-preserving record linkage BMC Journal of Translational Medicine 2021-01

Franke, M.; Sehili, Z.; Rahm, E. PRIMAT: A Toolbox for Fast Privacy-preserving Matching PVLDB 12(12): 1826-1829 2019-08

Franke, Martin; Gladbach, Marcel; Sehili, Ziad; Rohde, Florens; Rahm, Erhard ScaDS Research on Scalable Privacy-preserving Record Linkage Datenbank-Spektrum 2019-02

Franke, Martin; Sehili, Ziad; Gladbach, Marcel; Rahm, Erhard Post-processing Methods for High Quality Privacy-Preserving Record Linkage Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM 2018, CBT 2018. pp. 263–278. Lecture Notes in Computer Science, vol 11025. Springer 2018-09

Franke, Martin; Sehili, Ziad; Rahm, Erhard Parallel Privacy-Preserving Record Linkage using LSH-based blocking 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS), pp. 195-203, 2018 2018-03

Supervision of Thesis

Teaching

Lectures

• DBS2: 2019

Tutorials

• DBS1: WS 2020, WS 2019, WS 2018, WS 2017
• DBS2: SS 2022, SS 2021, SS 2020, SS 2019, SS 2018, SS 2017

Seminars

• New Trends in Machine Learning und Data Analytics WS 2021
• New Trends in Machine Learning and Data Analytics WS 2020
• Trends in Machine Learning and Data Analytics WS 2019
• Secure Data Processing WS 2018
• Deep Learning WS 2017

Labs

• Data-Warehouse & Data Mining: WS 2021, WS 2020, WS 2019, WS 2018, WS 2017
• Relational Databases: SS 2022, SS 2021, SS 2020, SS 2019, SS 2018, SS 2017
• Big Data: SS 2022, SS 2020, SS 2019, SS 2018