Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue, customizable privacy preservation based on differential privacy is a potentially promising countermeasure. However, we found that customizable protection will trigger the composition mechanism of differential privacy and leads to unexpected correlations among injected noises that weakens privacy protection and reveal more sensitive inforamtion. As a result, customizable privacy protection is vulnerable to two primary attacks: background knowledge attack and collusion attack. To optimize the tradeoff between customizable privacy preservation and data utility, we propose a customizable reliable differential privacy model (CRDP), which provides customizable protection on each individual while being attack-proof. We define social distance as the shortest path between two nodes, which is used as an index to customize the privacy protection levels, followed by quantitatively modeling the attacks under the framework of differential privacy. We develop a modified Laplacian mechanism in which the noise generation complies with a Markov stochastic process.Consequently, the correlations of noises are properly de-coupled so that CRDP can simultaneously minimize background knowledge attacks and eliminate collusion attacks in this particular scenario. The evaluation results from real-world datasets show the feasibility and superiority of CRDP in terms of customizable privacy preservation and reliable attack resistance.

中文翻译：

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网络物理社交网络中可定制的可靠隐私保护数据共享


由于海量数据在各种蓬勃发展的网络物理社交网络（CPSN）中不断共享，隐私泄露变得越来越严重。差异隐私是主流的隐私保护方法之一，但其大多数扩展都假设所有数据用户共享相同的隐私需求，这无法满足实践中的各种隐私期望。针对这一问题，基于差异隐私的可定制隐私保护是一种潜在的有前景的对策。然而，我们发现可定制的保护会触发差分隐私的合成机制，并导致注入的噪声之间出现意想不到的相关性，从而削弱隐私保护并泄露更多敏感信息。因此，可定制的隐私保护容易受到两种主要攻击：背景知识攻击和共谋攻击。为了优化可定制的隐私保护和数据效用之间的权衡，我们提出了一种可定制的可靠差分隐私模型（CRDP），该模型为每个人提供可定制的保护，同时防攻击。我们将社交距离定义为两个节点之间的最短路径，以此作为定制隐私保护级别的指标，然后在差分隐私框架下对攻击进行定量建模。我们开发了一种改进的拉普拉斯机制，其中噪声的生成符合马尔可夫随机过程。因此，噪声的相关性被适当地解耦，使得 CRDP 可以同时最小化背景知识攻击并消除这种特定场景中的共谋攻击。 真实数据集的评估结果表明了CRDP在可定制的隐私保护和可靠的攻击抵抗方面的可行性和优越性。