Tensor completion aims at filling the missing or unobserved entries based on partially observed tensors. However, utilization of the observed tensors often raises serious privacy concerns in many practical scenarios. To address this issue, we propose a solid and unified framework that contains several approaches for applying differential privacy to the two most widely used tensor decomposition methods: i) CANDECOMP/PARAFAC~(CP) and ii) Tucker decompositions. For each approach, we establish a rigorous privacy guarantee and meanwhile evaluate the privacy-accuracy trade-off. Experiments on synthetic and real-world datasets demonstrate that our proposal achieves high accuracy for tensor completion while ensuring strong privacy protections.

翻译：为了解决这一问题,我们提议了一个坚实和统一的框架,其中包含对两种最广泛使用的高压分解方法适用不同隐私的几种方法:(一) CANDECOMP/PARAFAC~(CP)和(二) Tucker分解方法。对于每一种方法,我们都建立严格的隐私保障,并同时评估隐私的准确性交易。对合成和现实世界数据集的实验表明,我们的提案在确保强力保护隐私的同时,实现了高精度完成。