Shuffle differential privacy (shuffle DP) offers an attractive distributed alternative to standard differential privacy. It uses a secure shuffler to permute users' randomized encodings, providing individual data privacy without a central trusted entity. A key challenge, however, is to achieve both generality and client efficiency. Under information-theoretic shuffle-DP guarantees, protocols that nearly match central-model utility are restricted to statistical tasks such as summation and histogr