Guest Editor(s)
Assoc. Prof. Wen-Chen Hu
Email: wen.chen.hu@und.edu
Affiliation: School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, United States
Homepage:
Research Interests: (mobile) data research and applications such as (mobile) data security & mining, and mobile/smartphone/spatial/web computing
Dr. Sanjaikanth E Vadakkethil Somanathan Pillai
Email: s.evadakkethil@und.edu
Affiliation: School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, United States
Homepage:
Research Interests: artificial intelligence, machine learning, security, privacy, mobile networks
Prof. Piyush Kumar Pareek
Email: piyush.kumar@nmit.ac.in
Affiliation: Department of Artificial Intelligence and Machine Learning, Nitte Meenakshi Institute of Technology, Bengaluru, India
Homepage:
Research Interests: software engineering, data Science, data compression, image processing, deep learning
Assist. Prof. Preetisudha Meher
Email: preetisudha@nitap.ac.in
Affiliation: Department of Electronics and Communication Engineering
National Institute of Technology, Arunachal Pradesh, India
Homepage:
Research Interests: low power VLSI, digital VLSI, embedded systems, IoT, bioinformatics , AIML
Summary
As artificial intelligence systems increasingly process sensitive personal and organizational data, the need for robust privacy-preserving techniques has become paramount. Encryption and differential privacy have emerged as foundational pillars for securing AI applications, enabling computation on protected data while providing mathematical guarantees against information leakage.
This special issue aims to bring together cutting-edge research addressing the intersection of encryption technologies and differential privacy mechanisms in modern computing systems. We seek contributions that advance both theoretical foundations and practical implementations, fostering innovation in privacy-preserving computation.
Suggested Themes
· Homomorphic Encryption for Machine Learning
· Differential Privacy in Deep Learning and Data Analytics
· Secure Multi-Party Computation Protocols
· Privacy-Preserving Federated Learning
· Encrypted Search and Database Systems
· Post-Quantum Cryptographic Solutions
· Privacy Budget Management and Composition Theorems
Keywords
encryption, differential privacy, homomorphic encryption, secure multi-party computation, privacy-preserving machine learning, cryptographic protocols, data confidentiality, federated learning
Published Papers
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