PhD position on ‘Adaptive Privacy and Differential Privacy for Energy Data’

As a PhD candidate, you will develop methods for adaptive privacy protection of energy data, combining differential privacy, empirical privacy attacks and privacy–utility optimisation. Your research is part of the Adaptive Privacy and Legal Compliance work package in the SHARE project. More specifically, your challenges are to:

• Investigate how temporal patterns, spatial information, rare events and other distinctive features contribute to re-identification and inference risk. You will work with metrics such as uniqueness, entropy, attack success rates and time-series-specific indicators.
• Implement and study attacks such as membership inference, reconstruction, re-identification and attribute inference to understand where information leakage occurs in both original and synthetic datasets.
• Investigate methods that allocate stronger protection to sensitive components and lighter perturbation to lower-risk components, thereby preserving as much analytical utility as possible.
• Formulate privacy protection as an optimisation problem balancing formal privacy guarantees against statistical, temporal and physical utility requirements for energy-system applications.
• Collaborate closely with researchers at Radboud University, who develop physics-informed generative models for synthetic energy data, and investigate approaches such as differentially private training and post-generation privacy calibration.
• Work closely with distribution system operator Alliander and other consortium partners to evaluate whether the developed methods provide meaningful protection while retaining the information required for practical energy-system applications.
• Ultimately contribute to an open-source privacy layer for the SHARE toolbox.