Balancing Privacy: Leveraging Privacy Budgets for Privacy-Enhancing Technologies
summary
Argues that cryptographic PETs (MPC, FHE, TEEs) protect input but not output privacy and proposes concrete mitigation: treat mixed deployments as hybrid PETs workflows, compile functions to estimate consumption before execution, and track a global plus local privacy budget across parties. Shows empirical hit to utility — epsilon=1 reduced a fraud model’s accuracy by 15–20% — and describes an open-source FHE library and a prototype dynamic privacy-budget calculator for proactive allocation and postcompute accounting.
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chapters (11)
- 0:00Hybrid PETs introduction
- 1:11Input versus output privacy
- 2:34Utility and precision balance
- 3:38PETs overview and limits
- 5:06Privacy budget basics
- 7:56Quantifying privacy budgets
- 9:18Allocating and monitoring budgets
- 10:44Privacy-utility trade-offs
- 12:52Calculating output bits
- 13:35Communicating budgets
- 14:16Conclusion and future
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