As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

中文翻译：

---

跨平台强大的评论发布隐私保护机制

作为评论系统，Crowd-Sourced Local Businesses Service System (CSLBSS) 允许用户公开发布包含显示名称、头像和评论内容的企业评论。在这些评论可以维护商业声誉并为他人提供有价值的参考的同时，攻击者也可以合法地获取用户的显示名称和大量历史评论。对于这个问题，我们表明对手可以通过利用获取的显示名称和历史评论发起连接用户身份攻击（CUIA）和统计推断攻击（SIA）来获取用户隐私。然而，现有的基于匿名和抑制评论的方法无法抵抗这两种攻击。此外，抑制评论可能会导致一些有用的评论不被发布。为了解决这些问题，我们提出了一种基于部分公开和完全匿名机制的跨平台强隐私保护机制（CSPPM）。在CSPPM中，基于用户评分和商业评分的一致性，我们提出了一种部分发布机制，发布评论有用性更高的评论，过滤虚假或不真实的评论。它确保我们的机制不会因评论的更高实用性而抑制评论并提高系统效用。我们还提出了一个完整的匿名机制来匿名化公开发布的评论的显示名称和头像。它确保对手无法通过 CUIA 和 SIA 获取用户隐私。最后，我们从理论和实验两个方面评估 CSPPM。结果表明，它可以抵抗CUIA和SIA，提高系统效用。