This paper examines at common machine learning applications in the physical security environment which collect data and what that means in light of GDPR by 1) providing general machine learning and vulnerability understanding, 2) examining intelligent systems in physical security, 3) investigating existing privacy standards in ML and, 4) evaluating regulatory implications on applied ML for enhanced physical security. We conclude this paper by providing a checklist of potential privacy violations of machine learning models during the process of data collection mapped to GDPR articles, to allow businesses to assess their need for privacy-preserving measures when enhancing their physical security posture through ML.
All rights reserved. Collection is open for research.
Citation
Petek Unsal, Saniye,
"Privacy and Machine Learning in Physical Security"
(2019).
Master of Science in Cybersecurity.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/ttky-m531
Brown's Master of Science (ScM) in Cybersecurity is a program for professionals designed to cultivate high-demand, industry executives with the unique and critical ability to devise and execute integrated, comprehensive cybersecurity strategies. Students gain immediately applicable knowledge and, through an …
