X-Vine：使用社交网络的安全和匿名路由
X-Vine: Secure and Pseudonymous Routing Using Social Networks
摘要：
分布式哈希表存在多个安全和隐私漏洞，包括Sybil攻击问题。现有的基于社交网络的解决方案，以减轻在DHT路由的Sybil攻击有一个高的状态要求，并没有提供足够的隐私级别。例如，这样的技术要求用户揭示他们的社交网络联系人。我们设计了X-Vine，这是一种分布式哈希表的保护机制，完全通过社交网络链接进行通信。与传统的点对点系统一样，X-Vine提供了健壮性、可扩展性和创新平台。使用社交网络链接进行通信有助于保护参与者的隐私，并增加了以前设计中缺乏的新的信任维度。X-Vine能够抵御Sybil攻击造成的拒绝服务攻击，事实上，它是第一个只需要每个节点对数数量的状态的Sybil防御，因此适合大规模和动态设置。X-Vine还有助于保护用户社交网络联系人的隐私，并将其IP地址隐藏在社交圈之外，为匿名通信提供基础。我们首先评估我们的设计与分析和模拟，使用几个现实世界的大型社交网络拓扑结构。我们表明，X-Vine的约束允许每个攻击边缘只插入对数数量的Sybil身份;我们表明，这减轻了恶意攻击的影响，同时不影响诚实节点的性能。此外，我们的算法是有效的，保持低拉伸，并避免在网络中的热点。我们用PlanetLab实现和Facebook插件验证我们的设计。
英文摘要：
Distributed hash tables suffer from several security and privacy vulnerabilities, including the problem of Sybil attacks. Existing social network-based solutions to mitigate the Sybil attacks in DHT routing have a high state requirement and do not provide an adequate level of privacy. For instance, such techniques require a user to reveal their social network contacts. We design X-Vine, a protection mechanism for distributed hash tables that operates entirely by communicating over social network links. As with traditional peer-to-peer systems, X-Vine provides robustness, scalability, and a platform for innovation. The use of social network links for communication helps protect participant privacy and adds a new dimension of trust absent from previous designs. X-Vine is resilient to denial of service via Sybil attacks, and in fact is the first Sybil defense that requires only a logarithmic amount of state per node, making it suitable for large-scale and dynamic settings. X-Vine also helps protect the privacy of users social network contacts and keeps their IP addresses hidden from those outside of their social circle, providing a basis for pseudonymous communication. We first evaluate our design with analysis and simulations, using several real world large-scale social networking topologies. We show that the constraints of X-Vine allow the insertion of only a logarithmic number of Sybil identities per attack edge; we show this mitigates the impact of malicious attacks while not affecting the performance of honest nodes. Moreover, our algorithms are efficient, maintain low stretch, and avoid hot spots in the network. We validate our design with a PlanetLab implementation and a Facebook plugin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
