Combinatorial Privacy: Private Multi-Party Bitstream Grand Sum by Hiding in Birkhoff Polytopes
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Abstract (arXiv)
We introduce PolyVeil, a protocol for private Boolean summation across k clients that encodes private bits as permutation matrices in the Birkhoff polytope. A two-layer architecture gives the server perfect simulation-based security (statistical distance zero) while a separate aggregator faces #P-hard likelihood inference via the permanent and mixed discriminant. Two variants (full and compressed) differ in what the aggregator observes. We develop a finite-sample (varepsilon,δ)-DP analysis with explicit constants. In the full variant, where the aggregator sees a doubly stochastic matrix per client, the log-Lipschitz constant grows as n4 Kt and a signal-to-noise analysis shows the DP guarantee is non-vacuous only when the private signal is undetectable. In the compressed variant, where the aggregator sees a single scalar, the univariate density ratio yields non-vacuous varepsilon at moderate SNR, with the optimal decoy count balancing CLT accuracy against noise concentration. This exposes a fundamental tension. #P-hardness requires the full matrix view (Birkhoff structure visible), while non-vacuous DP requires the scalar view (low dimensionality). Whether both hold simultaneously in one variant remains open. The protocol needs no PKI, has O(k) communication, and outputs exact aggregates.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 24 Mar 2026.
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References · 3
- 2018Distributed Differential Privacy via Shufflingcited by 115
- 2020InstaHide: Instance-hiding Schemes for Private Distributed Learningcited by 90
- 2019The Privacy Blanket of the Shuffle Modelcited by 73
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