The shuffle model of differential privacy (DP) has recently emerged as a powerful one for decentralized computation without fully trustable parties. Since it anonymizes and permutes messages from clients through a shuffler, the privacy can be amplified and utility can be improved. However, the shuffling procedure in turn restricts its applications only to statistical tasks that are permutation-invariant. This work explores the feasibility of shuffle privacy amplification for prevalent non-statistical computations: spatial crowdsourcing, combinatorial optimization, location-based social systems, and federated learning with incentives, which suffer either computationally intractability or intolerable utility loss in existing approaches (e.g., secure MPC and local DP). We proposes a new paradigm of shuffle model that can provide critical security functionalities like message authorization and result access control, meanwhile maintaining the most of privacy amplification effects. It incurs almost the same computation/communication costs as the non-private setting, and permits the server to run arbitrary algorithms on (noisy) client information in plaintext. Our novel technique is introducing statistically random identity into DP and force identical random distribution on all clients, so as to support secure functionalities even after message shuffling and to maintain privacy amplification simultaneously. Given that existing DP randomizers fails in the new shuffle model, we also propose a new mechanism and prove its optimality therein. Experimental results on spatial crowdsourcing, location-based social system, and federated learning with incentives, show that our paradigm and mechanism is fast as non-private settings, while reducing up to 90% error and increasing utility performance indicates by 100%-300% relatively, and can be practical under reasonable privacy budget.

这项工作探讨了用于非统计计算的混洗隐私扩大的可行性，提出了一种新的混洗模型范例，并引入了统计上的随机标识，从而在保持隐私扩大同时实现关键的安全功能。实验结果表明，这种模型和机制在减少错误率、提高效用性能方面相对于非隐私设置快速并且实用。

超越统计估计：洗牌模型中的差分隐私个体计算