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On the Tradeoff Between Data-Privacy and Utility for Data Publishing

机译：关于数据发布数据隐私与实用程序之间的权衡

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摘要

A typical method for privacy-preserving data publishing mechanism is to add random noise to the original data for publishing. No matter what kind of noise is added, there is a chance that the original state can be estimated in a certain accuracy. The probability of the original data inferred by the malicious receiver in a given interval is measured by (α, β) -data-privacy. With random noise added to the original data, the utility of the published data will decrease. In this paper, we investigate the tradeoff between data privacy and data utility under (α,β) -data-privacy, aiming to seek an optimal noise distribution. To maximize the weighted sum of privacy and utility we prove that when the added noise is symmetric and the data utility is measured by l1 - or l2 -norm function, the optimal noise follows the uniform distribution. Then we further investigate the optimal noise to maximize data utility with a certain privacy guarantee and we derive that the optimal noise is a group of impulse functions. Finally, we compare (α, β) -data-privacy with differential privacy and obtain the inequality relationship between the two privacy parameters. Simulations are conducted to validate the correctness of the obtained results.
机译：保留数据发布机制的典型方法是将随机噪声添加到原始数据以进行发布。无论添加什么样的噪音，都有可能以一定的准确性估计原始状态。通过（α，β）-data-privacy来测量由恶意接收器推断的原始数据的概率。随着随机噪声添加到原始数据，已发布数据的实​​用程序将减少。在本文中，我们调查（α，β）-data隐私下的数据隐私和数据实用程序之间的权衡，旨在寻求最佳的噪声分布。为了最大化隐私和实用程序的加权之和，我们证明了添加噪声是对称的，并且数据实用程序由l测量 1 - 或L. 2 -norm功能，最佳噪声遵循均匀分布。然后，我们进一步调查了最佳噪声，以最大化数据实用程序，具有一定的隐私保障，我们得出最佳噪声是一组脉冲函数。最后，我们与差异隐私进行比较（α，β） - 统计隐私，并获得两个隐私参数之间的不等式关系。进行仿真以验证所获得的结果的正确性。

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