Logistic regression over encrypted data from fully homomorphic encryption.
Logistic regression over encrypted data from fully homomorphic encryption.
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DOI：
10.1186/s12920-018-0397-z
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发表时间：
2018-10-11
影响因子：
2.7
通讯作者：
Lauter K
中科院分区：
文献类型：
--
作者：
Chen H;Gilad-Bachrach R;Han K;Huang Z;Jalali A;Laine K;Lauter K
2017年iDASH安全基因组分析竞赛的任务之一是在加密的基因组数据上训练逻辑回归模型。更准确地说，给定一个大约1500个患者记录的列表，每个记录都有18个包含特定突变信息的二进制特征，这个想法是让数据保持器使用同态加密来加密记录，并将它们发送到不可信的云存储。然后，云可以对加密数据同态地应用训练算法以获得加密的逻辑回归模型，该逻辑回归模型可以被发送到数据保持器以进行解密。通过这种方式，数据保持器可以成功地将训练过程外包，而不会将她的敏感数据或训练模型泄露给云。我们的解决方案有几个新颖之处：我们使用一个多位的纯文本空间在全同态加密与固定点数编码;我们结合联合收割机自举在全同态加密与缩放操作在固定点算术;我们使用一个极大极小多项式近似的S形函数和1位梯度下降法，以减少明文的增长在训练过程中。我们的加密数据训练算法每次迭代梯度下降需要0.4-3.2小时。我们证明了加密数据训练的可行性，但计算成本高。另一方面，我们的方法可以在关键应用程序中保证最高级别的数据隐私。
One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud. Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process. Our algorithm for training over encrypted data takes 0.4–3.2 hours per iteration of gradient descent. We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.
影响因子：
2.5
作者：
FRASER, W
通讯作者：
FRASER, W