Anonymous data sharing has been becoming more challenging in today's interconnected digital world, especially for individuals that have both anonymous and identified online activities. The most prominent example of such data sharing platforms today are online social networks (OSNs). Many individuals have multiple profiles in different OSNs, including anonymous and identified ones (depending on the nature of the OSN). Here, the privacy threat is profile matching: if an attacker links anonymous profiles of individuals to their real identities, it can obtain privacy-sensitive information which may have serious consequences, such as discrimination or blackmailing. Therefore, it is very important to quantify and show to the OSN users the extent of this privacy risk. Existing attempts to model profile matching in OSNs are inadequate and computationally inefficient for real-time risk quantification. Thus, in this work, we develop algorithms to efficiently model and quantify profile matching attacks in OSNs as a step towards real-time privacy risk quantification. For this, we model the profile matching problem using a graph and develop a belief propagation (BP)-based algorithm to solve this problem in a significantly more efficient and accurate way compared to the state-of-the-art. We evaluate the proposed framework on three real-life datasets (including data from four different social networks) and show how users' profiles in different OSNs can be matched efficiently and with high probability. We show that the proposed model generation has linear complexity in terms of number of user pairs, which is significantly more efficient than the state-of-the-art (which has cubic complexity). Furthermore, it provides comparable accuracy, precision, and recall compared to state-of-the-art.

中文翻译：

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社交网络中配置文件匹配风险的有效量化

在当今互联的数字世界中，匿名数据共享变得越来越具有挑战性，尤其是对于同时进行匿名和已识别在线活动的个人而言。当今此类数据共享平台最突出的例子是在线社交网络 (OSN)。许多个人在不同的 OSN 中有多个配置文件，包括匿名和已识别的（取决于 OSN 的性质）。在这里，隐私威胁是个人资料匹配：如果攻击者将个人的匿名个人资料与其真实身份联系起来，则可以获得隐私敏感信息，这可能会产生严重的后果，例如歧视或勒索。因此，量化并向 OSN 用户展示这种隐私风险的程度非常重要。现有的在 OSN 中对配置文件匹配进行建模的尝试对于实时风险量化来说是不够的并且计算效率低下。因此，在这项工作中，我们开发了算法来有效地建模和量化 OSN 中的配置文件匹配攻击，作为迈向实时隐私风险量化的一步。为此，我们使用图对配置文件匹配问题进行建模，并开发了一种基于置信传播 (BP) 的算法，以与最先进的技术相比，以更高效、更准确的方式解决此问题。我们在三个现实生活数据集（包括来自四个不同社交网络的数据）上评估了所提出的框架，并展示了如何高效且高概率地匹配不同 OSN 中的用户配置文件。我们表明，所提出的模型生成在用户对数量方面具有线性复杂性，这比最先进的（具有三次复杂性）更有效。此外，与最先进的技术相比，它提供了可比的准确度、精确度和召回率。