Ecenaz Erdemir received her B.Sc. and M.Sc. degrees in Electrical and Electronics Engineering from Middle East Technical University (METU) in 2014 and 2017, respectively. She has completed her 6 month internship with Amazon Web Services (AWS) as an Applied Science Intern in 2021. She is now pursuing a Ph.D. in the Department of Electrical and Electronic Engineering at Imperial College London (ICL). She has done research on convex optimization, path planning and localization in wireless sensor networks. Her current research interests are on information theory, privacy, cybersecurity, adversarial learning and reinforcement learning.
You can follow her publications and citations on the Google Scholar page.
Below is a list of her publications.
→ E. Erdemir, D. Gündüz and P. L. Dragotti, Smart meter privacy, in F. Farokhi (editor), Privacy in Dynamical Systems, Springer, 2020.
→ E. Erdemir, P. L. Dragotti and D. Gündüz, Privacy-aware time-series data sharing with deep reinforcement learning, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 389-401, 2021.
→ E. Erdemir, T. E. Tuncer, Path planning for mobile-anchor based wireless sensor network localization: Static and dynamic schemes, Ad Hoc Networks, vol. 77, pp. 1-10, Aug. 2018.
→ E. Erdemir, P. L. Dragotti and D. Gündüz, Active privacy-utility trade-off against a hypothesis testing adversary, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), June 2021.
→ E. Erdemir, P. L. Dragotti and D. Gündüz, Privacy-aware location sharing with deep reinforcement learning, IEEE Workshop on Information Forensics and Security (WIFS), Delft, Netherlands, Dec. 2019.
→ E. Erdemir, P. L. Dragotti and D. Gündüz, Privacy-cost trade-off in a smart meter system with a renewable energy source and a rechargeable battery, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, 2019.