Privacy-preserving authentication in participatory sensing systems

Abstract

Participatory Sensing Systems (PSS) are a type of Mobile Crowdsensing Systems where users voluntarily participate in contributing information. Task initiators create tasks, targeting specific data that needs to be gathered by the users device sensors. Such systems have been designed with different requirements, such as data trustworthiness, accountability and incentives, in a secure and private way. However, it is complex to protect user privacy without affecting the performance of the rest of the system. For example, with task assignment, either the user authenticates anonymously, or discloses its sensors for an efficient allocation. If the user identity is hidden to the system, it could happen that it receives a task it does not have the capability to perform. This thesis goal is to design an anonymous authentication model for PSS based on privacy preserving attribute based signatures. The proposed solution allows the Participatory Sensing System to enforce sensor requirements for an efficient task allocation. In addition to the design, experiments measuring the performance of the operations are included in the thesis, to prove it is suitable for real world scenarios.