Revisiting Continuous Noise Sampling for Multi-Party Differential Privacy

• URL: http://arxiv.org/abs/2608.27766v1
• Date: Thu, 27 Aug 2026 22:57:31 GMT
• Title: Revisiting Continuous Noise Sampling for Multi-Party Differential Privacy
• Abstract summary: In this paper, we revisit the continuous noise sampling protocols and present several improvements in both security and efficiency.<n>We demonstrate that the scaling operation in arithmetic circuits the noise to a sparse, publicly known set of values, so that an adversary can observe the released noisy queries and decide which dataset produced them.<n>Our implementation achieves $4times sim 612times$ speedup over existing secure discrete samplers and orders-of-magnitude speedup over the insecure sample-and-scale paradigm.
• Score: 5.859502071587914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Combining secure multi-party computation (MPC) with differential privacy (DP) enables multiple parties to release aggregate statistics without a trusted curator, and the core primitive is the protocol to sample noise from a continuous distribution under finite-precision arithmetic. In this paper, we revisit the continuous noise sampling protocols and present several improvements in both security and efficiency. We start by identifying a vulnerability in widely used sample-and-scale constructions. We demonstrate that the scaling operation in arithmetic circuits confines the noise to a sparse, publicly known set of values, so that an adversary can observe the released noisy queries and decide which dataset produced them. As concrete demonstrations, we instantiate attacks on two systems employing such ``flawed'' sampling protocols: Orchard (OSDI'20) for DP secure aggregation and DP-BREM$^+$ (USENIX Sec'25) for DP federated learning. We report a near-$100\%$ attack success rate on both systems, under any noise scaler $s\geq 2$ used in practice. The leakage we reveal is intrinsic to the scaling operation, and direct repairs either substantially sacrifice utility or add significant precision bits to make the sampling more expensive. To address the security and efficiency issues together, we turn to discrete sampling at the granularity of individual biased bits. We make several optimizations to the sampler and prove its security. Our implementation achieves $4\times \sim 612\times$ speedup over existing secure discrete samplers and orders-of-magnitude speedup over the insecure sample-and-scale paradigm, with negligible utility loss compared to the ideal continuous mechanism.

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