浮点算术下的安全多方计算
Secure multiparty computations in floating-point arithmetic
AI总结：
本文证明仅用标准浮点算术即可实现隐私保护机器学习的安全多方计算，其信息泄露小于舍入误差且具备最坏情况界与数值稳定性证明，并在多种广义线性模型上验证了高性能。
摘要：
安全多方计算使得能够将敏感数据的所谓份额分发给多方，从而使多方可以有效地处理数据，同时无法收集到关于数据的太多信息（至少在所有方没有串通起来将所有份额重新拼合的情况下不会）。因此，各方可以约定在计算结束时将其所有处理结果发送给受信任的第三方（可能是数据提供者），只有该受信任第三方能够查看最终结果。事实证明，仅使用标准浮点算术即可实现用于隐私保护机器学习的安全多方计算，至少在信息泄露受到谨慎控制、且小于舍入所致精度损失的情况下可以做到；所有这些都有严格的数学证明作为支撑，这些证明给出了有限精度算术中信息损失和数值稳定性的最坏情况界。数值示例展示了在商用现成硬件上针对广义线性模型所获得的高性能，这些模型包括普通线性最小二乘回归、二项及多项 logistic 回归、probit 回归和 Poisson 回归。
英文摘要：
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares). Thus, the parties may conspire to send all their processed results to a trusted third party (perhaps the data provider) at the conclusion of the computations, with only the trusted third party being able to view the final results. Secure multiparty computations for privacy-preserving machine-learning turn out to be possible using solely standard floating-point arithmetic, at least with a carefully controlled leakage of information less than the loss of accuracy due to roundoff, all backed by rigorous mathematical proofs of worst-case bounds on information loss and numerical stability in finite-precision arithmetic. Numerical examples illustrate the high performance attained on commodity off-the-shelf hardware for generalized linear models, including ordinary linear least-squares regression, binary and multinomial logistic regression, probit regression, and Poisson regression.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
