Jo-DPMF: Differentially private matrix factorization learning through joint optimization
Jo-DPMF: Differentially private matrix factorization learning through joint optimization
复制标题
Jo-DPMF：通过联合优化进行差分私有矩阵分解学习
DOI：
10.1016/j.ins.2018.07.070
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发表时间：
2018-10-01
影响因子：
8.1
通讯作者：
Choo, Kim-Kwang Raymond
中科院分区：
文献类型：
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作者：
Zhang, Feng;Lee, Victor E.;Choo, Kim-Kwang Raymond
随机梯度下降（SGD）是一种广泛使用的实现矩阵分解的技术。基于SGD的矩阵分解涉及许多迭代计算。因此，根据差分隐私的顺序合成理论，差分隐私矩阵分解的常规实现策略可能导致显著的误差累积，无论是将拉普拉斯噪声添加到原始矩阵还是添加到分解后的矩阵。事实上，差分私有矩阵分解的实现是如此具有挑战性，以至于迄今为止提出的结果存在隐私和数据效用效率低下的问题。在本文中，我们采用的目标摄动法来解决的挑战，这种方法显着地通过扰动目标函数，而不是扰动的结果，消除了误差积累。我们的方法优于国家的最先进的方法，因为它只需要一个标量噪声，而不是一个矢量噪声，以实现相同幅度的隐私。此外，我们的方法可以学习的结果矩阵的联合优化，遵循传统的SGD学习过程，并尽可能优化其收敛速度和精度。除了差分隐私保证，我们还经验性地展示了新模型与k-coRating（一种类似k-匿名的隐私保护模型）一起工作的方式，以提高数据效用。(C)2018爱思唯尔公司All rights reserved.
Stochastic gradient descent (SGD) is a widely-used technique to implement matrix factorization. SGD-based matrix factorization involves many iterative computations. Therefore, according to the sequential composition theory of differential privacy, conventional implementation strategies of differentially private matrix factorization may lead to significant error accumulation, no matter whether the Laplace noise is added to the original matrix or to the factorized matrices. In fact, the implementation of differentially private matrix factorization is so challenging that results proposed to date have the problem of inefficient privacy and data utility. In this paper, we employ the objective perturbation method to address the challenge; this method dramatically alleviates error accumulation by perturbing the objective function instead of perturbing the results. Our method outperforms the state-of-the-art methods since it only requires a scalar noise rather than a vector noise to achieve the same magnitude of privacy. Furthermore, our method may learn the resulted matrices by joint optimization, which follows the conventional learning procedure of SGD and optimizes its convergence speed and accuracy as much as possible. In addition to the differential privacy guarantee, we also empirically show the way that the novel model works together with k-coRating, a k-anonymity-like privacy preserving model, to enhance data utility. (C) 2018 Elsevier Inc. All rights reserved.