Security and Privacy in Heterogeneous Wireless and Mobile Networks: Challenges and Solutions

abstract: The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.Dissertation/ThesisPh.D. Electrical Engineering 201

User-centric privacy preservation in Internet of Things Networks

Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Game Theory Based Privacy Protection for Context-Aware Services

In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information