论文标题
机器学习环境中的基于私有M波段小波的机制
Differentially Private M-band Wavelet-Based Mechanisms in Machine Learning Environments
论文作者
论文摘要
在后工业世界中，数据科学和分析在数字数据隐私方面至关重要。即使对手对用户的初步知识很少，为访问数据集建立隐私的方法不当会损害大量用户数据。许多研究人员一直在开发高级隐私的机制，这些机制也保留了数据的统计完整性以应用于机器学习。差异隐私的最新发展（例如拉普拉斯和特权机制）大大降低了对手可以区分数据集中元素并从而提取用户信息的概率。在本文中，我们使用离散的M波段小波变换开发了三种隐私的机制，将噪声嵌入到数据中。前两种方法（LS和LS+）通过Laplace-Sigmoid分布增加了噪声，该分布将拉普拉斯分布的值乘以Sigmoid函数，而第三种方法则利用伪Quantum隐志将噪声嵌入到数据中。然后，我们证明我们的机制通过各种机器学习环境中的统计分析成功地保留了差异隐私和可学习性。
In the post-industrial world, data science and analytics have gained paramount importance regarding digital data privacy. Improper methods of establishing privacy for accessible datasets can compromise large amounts of user data even if the adversary has a small amount of preliminary knowledge of a user. Many researchers have been developing high-level privacy-preserving mechanisms that also retain the statistical integrity of the data to apply to machine learning. Recent developments of differential privacy, such as the Laplace and Privelet mechanisms, drastically decrease the probability that an adversary can distinguish the elements in a data set and thus extract user information. In this paper, we develop three privacy-preserving mechanisms with the discrete M-band wavelet transform that embed noise into data. The first two methods (LS and LS+) add noise through a Laplace-Sigmoid distribution that multiplies Laplace-distributed values with the sigmoid function, and the third method utilizes pseudo-quantum steganography to embed noise into the data. We then show that our mechanisms successfully retain both differential privacy and learnability through statistical analysis in various machine learning environments.
