Scheduling of differential privacy

In this project, we enabled Kubernetes/Kubeflow to manage differential privacy (DP) budget among jobs in ML workloads. In particular, our contributions are

  1. Characterize the privacy resource as dynamically-arriving, non-replenishable private blocks,
  2. Develop a new scheduling algorithm of DPF (Dominant Private block Fairness),
  3. Study game-theoretical properties of DPF.

My contributions are

  • Reframed the problem and enabled to decouple privacy scheduling from compute resource scheduling, proposed the original DPF algorithm, gave initial rigorous proofs of its game theoretical properties.
  • Derived a weaker condition for envy-freeness property, proposed alternative DP scheduling algorithms.
  • Proposed the key technique to adapt DPF algorithm to Rényi DP. Namely, RDP allocation curve only need to be partially bounded by unlocked RDP budget curve during scheduling.
  • Designed, implemented and fine-tuned a discrete-event simulator.
  • Algorithm prototyping and microbenchmarking - evaluated various scheduling algorithms via simulation experiment, investigated their tradeoffs.
Tao Luo
Tao Luo
CS PhD Student

My interests lie in performance and security aspects in distributed systems/networking.