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arXiv Machine Learning By Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling

Protect Your Score: Contact Tracing With Differential Privacy Guarantees

Read the original on arXiv Machine Learning →

The paper introduces a contact tracing algorithm that incorporates differential privacy to protect users’ risk scores from adversarial inference. By releasing each risk score with an epsilon of 1, the algorithm achieves a two- to ten-fold reduction in infection rates across two leading COVID‑19 agent‑based simulators. This work claims to be the first to provide differential privacy guarantees for risk score disclosure in COVID‑19 contact tracing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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