Search

Mobile Social Networks (MSNs) are a category of social networks that have features of both traditional OSNs and Location-Based Social Networks (LBSNs). MSNs provide users with an opportunity to explore social media, connect with people nearby, or with those sharing similar interests. The ease with which people can connect and use these networks to obtain information, share user-centric data, and request services on the go through their mobile devices as long as they have access to the internet has made MSNs omnipresent. Due to the widespread nature of MSNs and the lack of effective privacy-preserving architecture makes them a target for several attacks by adversaries and the users of the networks are completely vulnerable to these privacy breaches. With the advancements in technology, the number of attacks and their possible solutions is complex and expansive. Therefore, there is a need to properly categorize them to understand the intricacies of different attacks and the concepts used by researchers to devise different solutions. This survey on privacy issues in MSNs serves as a way to provide an introduction to MSNs. We categorize privacy in MSN on the basis of the aspect they effect and summarize the different threats in each category. Furthermore, we provide an elaborate classification of the various privacy-preserving solutions proposed in recent research works, under each category of privacy. We also review different datasets and data generation tools that can be used by future researchers in this field.

Mobile Crowd-Sensing (MCS) is a dominant sensing paradigm for Internet of Things (IoT), with a lot of potentials as it allows data collection through the user's sensor embedded mobile devices. The participation of people in IoT not only brings greater flexibility and sensing ability but also increases the risk of privacy breaches to the participants. Primarily, a worker's location data is vulnerable to information leaks as the task assignment in MCS is location-based. Most existing mechanisms that preserve worker's location in MCS are designed under the assumption that the platform is trusted, which may be not valid in real-world applications. Besides, the existing studies focus either on task selection problem for workers or task assignment problem for the platform. Therefore, this paper investigates both task bidding and assignment while preserving location privacy. We propose two task selection strategies: Minimize Total Cost (MTC) and Minimize Average Cost (MAC). Each worker submits a cost that is obfuscated using differential privacy to the platform. We propose probability cost-efficient worker selection mechanism (PCE-WSM) to determine winners and probability individual-rationality critical payment mechanism (PIR-CPM) to determine payments to winners. We prove that PIR-CPM is truthful and can achieve probability-individual rationality by theoretical analysis. To evaluate our proposed strategies, we conduct extensive experiments on both synthetic and real-world datasets, and the experimental results validate that PCE-WSM can achieve enough privacy preservation without incurring a high payment.

With the boom in the production of smartphones and easy access to networks, mobile crowd-sensing (MCS) is becoming a promising and rapidly growing sensing paradigm for the Internet of Things (IoT). In MCS, workers can get rewarded by participating in data collection while traveling to their destination from some starting point. However, the privacy leakage becomes a serious and unavoidable problem that hinders users' engagement. Taking task at a specific location or even going through some seemingly innocent locations may both disclose user's sensitive location information. Simply concealing or obfuscating the sensitive locations could preserve the privacy to a certain extent, at the cost of reward reduction, which will significantly frustrate the workers. To address these issues, this paper provides a novel framework for task selection in MCS jointly covering comprehensive location privacy preservation, efficient resource consumption, and high task profits. In our framework, workers can select their paths freely, and customize their privacy requirements for protection against both direct location disclosure and destination inference attack. Two corresponding algorithms are proposed for path selection under various stages of privacy preservation. The theoretical analysis demonstrates the performance of two algorithms on both privacy and utility aspects. We also evaluate our proposed mechanisms through extensive experiments on a real-world data set. The results validate that the proposed mechanisms can achieve higher task earnings and better privacy preservation.