Crowdsensing requires scalable privacy-preserving authentication that allows users to send anonymously sensing reports, while enabling eventual anonymity revocation in case of user misbehavior. Previous research efforts already provide efficient mechanisms that enable conditional privacy through pseudonym systems, either based on Public Key Infrastructure (PKI) or Group Signature (GS) schemes. However, previous schemes do not enable users to self-generate an unlimited number of pseudonyms per user to enable users to participate in diverse sensing tasks simultaneously, while preventing the users from participating in the same task under different pseudonyms, which is referred to as sybil attack. This paper addresses this issue by providing a scalable privacy-preserving authentication solution for crowdsensing, based on a novel pseudonym-based signature scheme that enables unlinkable-yet-accountable pseudonymity. The paper provides a detailed description of the proposed scheme, the security analysis, the performance evaluation, and details of how it is implemented and integrated into a real crowdsensing platform.

中文翻译：

---

一种用于保护隐私的人群感知的具有不可链接但可解释的假名的签名方案

Crowdsensing 需要可扩展的隐私保护身份验证，允许用户发送匿名感知报告，同时在用户不当行为的情况下启用最终的匿名撤销。先前的研究工作已经提供了有效的机制，通过基于公钥基础设施 (PKI) 或组签名 (GS) 方案的假名系统实现有条件的隐私。然而，之前的方案并没有让用户自己为每个用户生成无限数量的假名，让用户同时参与不同的感知任务，同时防止用户以不同的假名参与同一任务，这被称为sybil攻击。本文通过为人群感知提供可扩展的隐私保护身份验证解决方案来解决这个问题，基于一种新颖的基于假名的签名方案，该方案可实现不可链接但可负责的假名。本文详细描述了所提出的方案、安全性分析、性能评估，以及如何实施和集成到真正的人群感知平台中的细节。