## 1 Introduction  
Mobile social networks (MSNs) facilitate communication anytime and anywhere. However, mobile devices typically connect via mobile networks, which can struggle with coverage and capacity, leading to connectivity issues [1, 2, 3]. Opportunistic networks have emerged as a solution, allowing devices to communicate ad hoc through a store-carry-forward method. Messages are held until a suitable opportunity for delivery arises, relying on the cooperation of passing devices to transport and deliver messages [4]. These developments have fostered the creation of opportunistic MSNs (OMSNs), which are deeply integrated into social life by enabling new forms of social interaction via features like texting and photo sharing. However, this convenience comes at the cost of privacy. As messages are forwarded through intermediary nodes within these networks, personal data and user activities become increasingly vulnerable to exposure [5, 6, 7, 8]. Therefore, OMSN privacy implications must be balanced with their benefits [7, 9]. The challenge lies in preserving privacy without impeding the efficiency of social interactions. Traditional routing models in OMSNs prioritize metrics such as delivery rate, latency, and network overhead, often overlooking the critical aspect of security and privacy. Recently, there has been a great tendency to take advantage of social relationships in routing design to achieve more reliable message delivery by efficiently selecting the next hop of message forwarding [10; 11]. However, using social metrics to enhance privacy in OMSNs poses some challenges. The primary challenge is developing a system model that intelligently decides which nodes to trust with message forwarding. This involves quantifying trust using social metrics, which can be complex due to these networks' dynamic and decentralized nature. Trust can be asymmetric and context-dependent, further complicating the decision-making process. Additionally, integrating a privacy-aware solution with the legacy protocols poses the risk of altering the fundamental path construction logic, potentially affecting the network's overall efficiency. Moreover, extensive simulation and analysis are required to validate the effectiveness of privacy-preserving methods in diverse and realistic network scenarios. In response to these challenges, the paper introduces the following contributions. Firstly, it presents a privacy layer that can be integrated into existing routing decision models, empowering users to control the dissemination of their messages. This layer is designed to be lightweight and flexible, ensuring that the core logic of path construction remains intact. Secondly, the paper proposes three user-centric privacy-aware forwarding modes that leverage social metrics to guide the selection of intermediary nodes in the message-forwarding path. These modes are tailored to approximate real-world social relationship strengths, categorized as familiar, weak tie, or stranger. They are complemented by trust thresholds that users can adjust according to social contexts. Thirdly, the paper thoroughly evaluates the privacy enhancement and network performance implications of these modes through extensive simulations using the ONE simulator. The simulations cover a range of routing schemes and movement models, comprehensively assessing the proposed solutions. The results demonstrate that the proposed privacy-aware modes can significantly enhance user privacy by up to 45% in various network scenarios, striking a balance between privacy and network performance. These contributions collectively address the pressing need for privacy preservation in OMSNs and pave the way for more secure social networking experiences. The remainder of this paper is organized as follows. Section 2 surveys the related works. Section 3 presents our system model and methodology. Section 4 presents the proposed privacy-aware forwarding selection modes. Section 5 presents the simulation parameters and measurement metrics.