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Learn more about my work! Research Statement Teaching Statement Diversity Statement
Incoming Assistant Professor
NSF Fellow
Arizona State University
jaron.mink@asu.edu
I’m an incoming Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. I’m currently recruiting motivated PhD students; if you are interested in working together, send me an email!
My work discovers how human interaction impacts ML security in two ways: How human factors can be 1) exploited to reduce security and 2) harnessed to improve security. Since ML-enabled abuse is becoming increasingly common, I investigate how lay users perceive and react to new attacks, e.g., how social media users react to deepfakes [USENIX Sec. 2022, CHI 2024]. As ML is beginning to be applied in security-critical systems, I evaluate how usable these tools are for technical users, e.g., how easy it is for ML developers to apply security defenses [USENIX Sec. 2023, IEEE SP 2023].
I work at the intersection of usable security, machine learning, and system security. My Ph.D. is advised by Professor Gang Wang at the University of Illinois at Urbana-Champaign.
My CV is available here.
It’s Trying Too Hard To Look Real: Deepfake Moderation Mistakes and Identity-Based Bias
Jaron Mink, Miranda Wei, Collins W. Munyendo, Kurt Hugenberg, Tadayoshi Kohno, Elissa M. Redmiles, Gang Wang
ACM CHI Conference on Human Factors in Computing Systems, 2024 (CHI)
PDF Talk
“Security is not my field, I’m a stats guy”: A Qualitative
Root Cause Analysis of Barriers to Adversarial Machine
Learning Defenses in Industry
Jaron Mink*, Harjot Kaur*, Juliane Schmüser*, Sascha Fahl, Yasemin Acar
32nd USENIX Security Symposium, 2023 (USENIX Security)
PDF Talk Slides
Everybody’s Got ML, Tell Me What Else You Have: Practitioners’ Perception of
ML-Based Security Tools and Explanations
Jaron Mink, Hadjer Benkraouda, Limin Yang, Arridhana Ciptadi, Ali Ahmadzadeh, Daniel Votipka, Gang Wang
44th IEEE Symposium on Security and Privacy, 2023 (IEEE S&P)
PDF Talk
Slides Teaser
SoK: History is a Vast Early Warning System: Auditing the Provenance of System Intrusions
Muhammad Adil Inam, Yinfang Chen, Akul Goyal, Jason Liu, Jaron Mink, Noor Michael, Sneha Gaur, Adam Bates, Wajih Ul Hassan
44th IEEE Symposium on Security and Privacy, 2023 (IEEE S&P)
PDF
FAuST: Striking a Bargain between Forensic Auditing’s Security and Throughput
Muhammad Adil Inam, Akul Goyal, Jason Liu, Jaron Mink, Noor Michael, Sneha Gaur, Adam Bates, Wajih Ul Hassan
38th Annual Computer Security Applications Conference, 2022 (ACSAC)
PDF
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