Accurate, private, secure, federated U-statistics with higher degree
We study the problem of computing a U-statistic with a kernel function f of degree k $\ge$ 2, i.e., the average of some function f over all k-tuples of instances, in a federated learning setting. Ustatistics of degree 2 include several useful statistics such as Kendall's $τ$ coefficient, the Area under the Receiver-Operator Curve and the Gini mean difference. Existing methods provide solutions only under the lower-utility local differential privacy model and/or scale poorly in the size of the domain discretization. In this work, we propose a protocol that securely computes U-statistics of degree k $\ge$ 2 under central differential privacy by leveraging Multi Party Computation (MPC). Our ...
| Authors | Quentin Sinh, Jan Ramon |
|---|---|
| Primary category | cs.CR |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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