If we knew what it was we were doing, it would not be called research, would it?
- Albert Einstein
I am keenly interested in working on the challenging topics in Trustworthy AI, especially security and privacy in machine learning.
I have also gotten some research experience exploring some topics in Digital Image Forensics, Applied Cryptography, Computer Vision & Deep learning.
Activities and updates
08/22/23 Got selected to serve on Artifact Review Committee of PETS'24
06/20/23 Got selected as an Artifact Evaluation Committee member of NDSS'24
06/15/23 Got invited as a TPC member for Workshop on Cyberspace Security and Artificial Intelligence (CAI-2023) @ IEEE TrustComm'23
05/27/23 Enjoyed IEEE S&P'23 at San Fransisco
05/22/23 Our proposal on "A Design Study of Problem-centered Instruction (PCI) for Private Artificial Intelligence (AI) Curriculum Development" got accepted at the AECT International Convention, Orlando, FL.
04/26/23 Had a great time presenting our work on FENet at the ACM IWSPA @CODASPY'23.
04/21/23 Our poster "Packing-aware Pruning for Efficient Private Inference based on Homomorphic Encryption" got accepted for oral presentation at IEEE S&P 2023
02/08/23 Our paper "FENet: Privacy-preserving Neural Network Training with Functional Encryption" got accepted in the 9th ACM International Workshop on Security and Privacy Analytics co-located with ACM CODASPY 2023.
05/10/22 I'm invited to serve as a web chair for IEEE S&P (Oakland) 2023
04/20/22 Our poster "Privacy-preserving Neural Network with Functional Encryption" got accepted for oral presentation at IEEE S & P 2022 (Oakland)
04/17/22 Received a travel grant ($1300) to attend the most awaited IEEE Symposium on Security & Privacy 2022 that will be held at San Fransisco.
02/11/22 Attended a workshop on Private AI by Institute for Mathematical and Statistical Innovation, Chicago
11/17/21 Our paper "Towards Faster Functional Encryption for Privacy-preserving Machine Learning" got accepted in IEEE TPS 2021
02/02/21 Successfully passed PhD qualifier exam
05/20/21 Received a grant to attend IEEE Symposium on Security and Privacy 2021
01/15/21 Won 5th prize in Crypto Hackathon organized by FENTEC GROUP under EU H2020 project in conjunction with International Association for Cryptologic Research Real World Crypto Symposium 2021
11/13/20 Received a grant for and attended ACM CCS 2020
07/23/20 Attended Microsoft's Frontiers in Machine Learning conference
06/17/20 National Security Agency and the Department of Homeland Security have designated our center @Georgia State University as a National Center of Academic Excellence in Cyber Research
06/06/20 Got selected for and attended training on Designing a Data Science Solution on Azure organized by Microsoft Research Redmond
5/21/20 Attended 41st IEEE Symposium on Security and Privacy
4/29/20 Received a grant to attend 41st IEEE Symposium on Security and Privacy 2020
Prajwal Panzade and Daniel Takabi. FENet: Privacy-preserving Neural Network Training with Functional Encryption. IWSPA @ ACM CODASPY 2023. (supported by Microsoft Research and NSF)
Prajwal Panzade and Daniel Takabi. SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges.
Prajwal Panzade and Daniel Takabi. Towards Faster Functional Encryption for Privacy-preserving Machine Learning. IEEE International Conference on Trust, Privacy, and Security in Intelligent Systems, and Applications, Dec 2021. IEEE (supported by Microsoft Research and NSF).
Panzade PP, Prakash CS, Maheshkar S, Om H. Detection of Copy-move forgery using AKAZE and SIFT keypoint extraction. Journal on Multimedia Tools and Applications. May 2019. Springer.
Panzade PP, Prakash CS, Maheshkar S. Copy-move forgery detection by using HSV preprocessing and keypoint extraction. In Parallel, Distributed and Grid Computing (PDGC), 2016 Fourth International Conference on 2016 Dec 22 (pp. 264-269). IEEE.
Geethika S, Sai Sree I, Pranathi A, Prajwal P, Advertisements and Multimedia Recommendation based on Age, Gender and Emotion (AMRAGE). ICCCMLA 2019: International Conference on Cybernetics, Cognition, and Machine Learning Applications. Springer.
Prajwal Panzade and Daniel Takabi. Privacy-preserving Neural Network with Functional Encryption. IEEE Symposium on Security & Privacy 2022
Ghazvinian P, Podschwadt R, Panzade P, Rafiei MH, Takabi D. Packing-aware Pruning for Efficient Private Inference based on Homomorphic Encryption. IEEE Symposium on Security & Privacy 2023