positions
DC17: Privacy in Embedded AI
Task: Privacy in Embedded AI (WP1)
Host institution: UREAD
Country: United Kingdom
Supervisor: Prof. X. Chen [UREAD]
Co-supervisors: Prof. A. Asadi [TU Darstadt]; Dr. M. Petković [Philips]
Objectives: 1) To analyse and investigate the underlying model- and system-level causes for privacy leakage; 2) To design effective and lightweight privacy-preserving solutions; 3) To propose privacy-by-design frameworks.
Expected Results: 1) A comprehensive analysis of privacy vulnerabilities in SOTA embedded AI models and systems, and a deep understanding of the underlying causes; 2) Effective and lightweight privacy-preserving solutions along the Embedded AI pipeline; 3) Privacy by design frameworks for model compression and model deployment in Embedded AI systems and applications.
PhD enrolment: Doctoral School of UREAD
Planned secondments:
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Philips (3 months, M16-M18): Integrating privacy-by-design framework to mobile healthcare, with Prof. A. Asadi (KPI: joint paper)
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TU Delft (4 months, M30-M33): Lightweight privacy-preserving and security-enhancing methods for embedded AI, with Dr. M. Petković (KPI: joint paper)
Candidate profile: computer science, electrical engineering, telecommunication engineering, applied mathematics,, (in order of preference)
Desirable skills/interests: machine learning/deep learning, cyber security, embedded systems, data science (the applicant should be proficient in at least one or two of the skills)
Application Deadline: February 14, 2025, AoE
Descriptions of all the 18 DC Positions
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