Felix Fischer is a Research Associate and PhD student at the Chair of Cyber Trust.
He studies the interaction of people with information security and privacy technologies. This means analyzing their application, usability and acceptance in the wild. His most recent publications focus on software engineers struggling with getting cryptography right and explore machine learning as a tool for usable security and privacy.
In his current projects, he investigates how machine learning can assist people in protecting their privacy. His goal is to develop systems that learn how to process data in a way that achieves both, increased utility and privacy for the individual user.
Email: firstname.lastname@example.org | Phone: +49 (89) 289 - 17741 | Twitter: @fischerfel | GitHub: fischerfel | Open student projects
- Fischer, F., & Grossklags, J. (2022) Nudging Software Developers Toward Secure Code. IEEE Security and Privacy, 20(2), forthcoming.
- Felix Fischer, Yannick Stachelscheid, & Jens Grossklags (2021) The Effect of Google Search on Software Security: Unobtrusive Security Interventions via Content Re-ranking. Proceedings of the 28th ACM Conference on Computer and Communications Security (CCS), forthcoming. Author Version
- Felix Fischer, Huang Xiao, Ching-yu Kao, Yannick Stachelscheid, Benjamin Johnson, Danial Raza, Paul Fawkesley, Nat Buckley, Konstantin Böttinger, Paul Muntean & Jens Grossklags (2019) Stack Overflow Considered Helpful! Deep Learning Security Nudges Towards Stronger Cryptography, Proceedings of the 28th USENIX Security Symposium (USENIX Security), pp. 339-356. Acceptance rate = 16.2%. Author Version Open Access
- Mengsu Chen, Felix Fischer, Na Meng, Xiaoyin Wang & Jens Grossklags (2019) How Reliable is the Crowdsourced Knowledge of Security Implementation? Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE), pp. 536-547. Acceptance rate = 20.6%. Author Version Publisher Version
- Severin Engelmann, Mo Chen, Felix Fischer, Ching-yu Kao & Jens Grossklags (2019) Clear Sanctions, Vague Rewards: How China's Social Credit System Currently Defines “Good” and “Bad” Behavior. Proceedings of the 2nd ACM Conference on Fairness, Accountability, and Transparency (FAT*), Atlanta, Georgia, January 2019. Acceptance rate = 24.1%. Author Version Free Access (ACM Authorizer)
- Felix Fischer, Konstantin Böttinger, Huang Xiao, Christian Stransky, Yasemin Acar, Michael Backes & Sascha Fahl (2017) Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security. Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P), pp. 121-136. Acceptance rate = 13.1%. Publisher Version
- Sascha Fahl, Sergej Dechand, Henning Perl, Felix Fischer, Jaromir Smrcek & Matthew Smith (2014) Hey, NSA: Stay Away From My Market! Future Proofing App Markets Against Powerful Attackers. Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 1143-1155. Acceptance rate = 19.5%. Publisher Version
'Trust no one', The Register, 28 Jan 2020
From exposed to private with a single line of code, 19 June 2019
Beyond deepfakes - is there a GAN for good?, 1 May 2019
"Alexa, tell me my secrets", 28 Nov 2018