差分隐私相关论文集合

放一个论文合集

1、基于梯度的联邦学习方法,往往通过在每次迭代中随机地扰动中间输出来应用差分隐私(也就
是说在联邦学习的过程不会暴露是否使用某个特定的样本信息)

① DWORK C, ROTH A. The algorithmic foundations of differential privacy[J]. Foundations & Trends® in Databases, 2014, 9(3-4): 211-407.
② BASSILY R, SMITH A, THAKURTA A. Private empirical risk minimization: Efficient algorithms and tight error bounds[C]//2014 IEEE 55th Annual Symposium on Foundations of Computer Science. Piscataway: IEEE Press, 2014: 464-473.
③ PAPERNOT N, SONG S, MIRONOV I, et al. Scalable private learning with pate[J]. arXiv preprint, 2018, arXiv:1802.08908.

2、接1,现在实行的扰动方式特别多。对梯度数据添加了高斯噪声,采用了拉普拉斯噪声

① WU X, LI F G, Kumar A, et al. Bolt-on differential privacy for scalable stochastic gradient descent- based analytics[C]//The 2017 ACM International Conference on Management of Data. New York: ACM Press, 2017: 1307-1322.
② LUCA M, GEORGE D, EMILIANO DE C. Efficient private statistics with succinct sketches[J]. arXiv preprint, 2015, arXiv: 1508.06110.

3、Choudhury O等人成功将差分隐私部署在联邦学习框架内用来分析与健康相关的数据,但是在试验中证明,差分隐私可能会带来较大的函数损失值

CHOUDHURY O, GKOULALAS-DIVANIS A, SALONIDIS T, et al. Differential privacy-enabled federated learning for sensitive health data[J]. arXiv preprint, 2019, a rXiv: 1910.02578.

4、Geyer R C等人证明了 差分隐私对于保障数据持有方的数据隐私的有效性,同时认为大量的数据持有方会使带有差分隐私的联 邦学习表现更加稳定,准确率更高。

GEYER R C, KLEIN T, NABI M. Differentially private federated learning: a client level perspective[J]. arXiv preprint, 2017, arXiv:1712.07557.

5、Pettai M等人将安全多方计算与差分隐私技术相结合,用来保护来自不同 数据持有方的数据

PETTAI M, PEETER L. Combining differential privacy and secure multiparty computation[C]//The 31st Annual Computer Security Applications Conference. New York: ACM Press, 2015.

6、Jeong E 等人也设计了一种结合了安全多方计算与差分隐私技术的联邦学 习隐私保护系统,这种系统结合降噪差分隐私与加性同态加密,有效地保障了联邦学习系统的隐私性。

JEONG E, OH S, KIM H, et al. Communication-efficient on-device machine learning: federated distillation and augmentation under non-iid private data[J]. arXiv preprint, 2018, arXiv: 1811.11479.

7、Xu R H 等人提出了一种新的加密方法 HybridAlpha,将差分隐私技术和基于功能加密的安全多方计算结合,被证明拥有很好的通信效率。

XU R H, BARACALDO N, ZHOU Y, et al. HybridAlpha: an efficient approach for privacy-preserving federated learning[C]//The 12th ACM Workshop on Artificial Intelligence and Security. New York: ACM Press, 2019.

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