Participatory sensing is an emerging paradigm in which citizens voluntarily use their mobile phones to capture and share sensed data from their surrounding environment in order to monitor and analyze some phenomenon. Various reputation systems have been proposed to monitor participants' behavior in participatory sensing applications in order to identify those who provide bad contributions. However, the existing reputation systems do not provide privacy guarantees to the contributors. Thus, users usually hesitate to join participatory sensing campaigns since they are asked to provide their sensed data including time, location, etc. It has been shown in different works that up to 95% of participants’ identities can be re-identified through sharing four contributions containing multi-sensor data. Due to this reason, the integration between privacy preserving systems and reputation systems is a crucial need for building secure and reliable participatory sensing applications. This integration requires the assurance of seemingly conflicting objectives. Indeed, reputation systems monitor participants’ behaviors along subsequent interactions. Whereas, the objective of privacy preserving systems is to in fact detach the link between subsequent interactions. In this thesis, we study possible strategies to integrate privacy preserving systems within reputation-aware systems. This integration raises a new privacy challenge because of the contradiction between their objectives. Then, a new attack has been defined (RR attack). RR attack exploits this contradiction in order to detect the succession of contributions provided by the same participant. Next, a new privacy preserving reputation-aware protocol (PrivaSense) is proposed. PrivaSense defines a method that ensures both privacy and reputation and simultaneously solves their contradiction. Finally, we propose a novel reputation system DTSRS, which depends on a set of the most trusted participants in order to assess the reputation of participants. DTSRS introduces new measures to evaluate the trust of participants' contributions. DTSRS has the ability to defend more strongly against a set of attacks that was not considered in the literature (e.g. Collision, On-off, etc.). DTSRS enables the application server to aggregate the data more accurately. Moreover, DTSRS assesses both trust and reputation correctly even if a large number of adversaries are included in the sensing campaign.