Reading the Receipts: How Smarter Privacy Accounting Could Unlock More from Sensitive Data

Sophie Taylor and colleagues propose a method called realisation-level accounting to track privacy costs more accurately during data sharing for AI training, allowing systems to operate longer within the same privacy constraints. This approach measures actual privacy leakage rather than worst-case scenarios, potentially extending model training rounds without compromising patient privacy guarantees. Developers can now achieve more efficient use of their privacy budgets in federated learning and other sensitive data applications.

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