User-generated social media data are exploding and of high demand in public and private sectors. The disclosure of intact social media data exacerbates the threats to user privacy. In this paper, we first identify a text-based user-linkage attack on current data outsourcing practices, in which the real users in an anonymized dataset can be pinpointed based on the users' unprotected text data. Then we propose a framework for differentially privacy-preserving social media data outsourcing for the first time in literature. Within our framework, social media data service providers can outsource perturbed datasets to provide users differential privacy while offering high data utility to social media data consumers. Our differential privacy mechanism is based on a novel notion of E - text indistinguishability, which we propose to thwart the text-based user-linkage attack. Extensive experiments on real-world and synthetic datasets confirm that our framework can enable high-level differential privacy protection and also high data utility.

Online social networks (OSNs) are increasingly threatened by social bots which are software-controlled OSN accounts that mimic human users with malicious intentions. A social botnet refers to a group of social bots under the control of a single botmaster, which collaborate to conduct malicious behavior while mimicking the interactions among normal OSN users to reduce their individual risk of being detected. We demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation through real experiments on Twitter and also trace-driven simulations. We also propose the corresponding countermeasures and evaluate their effectiveness. Our results can help understand the potentially detrimental effects of social botnets and help OSNs improve their bot(net) detection systems.

Distributed stream monitoring has numerous potential applications in future smart cities. Communication efficiency, and data privacy are two main challenges for distributed stream monitoring services. In this paper, we propose PriStream, the first communication-efficient and privacy-preserving distributed stream monitoring system for thresholded PERCENTILE aggregates. PriStream allows the monitoring service provider to evaluate an arbitrary function over a desired percentile of distributed data reports and monitor when the output exceeds a predetermined system threshold. Detailed theoretical analysis and evaluations show that PriStream has high accuracy and communication efficiency, and differential privacy guarantees under a strong adversary model.

Influential users have great potential for accelerating information dissemination and acquisition on Twitter. How to measure the influence of Twitter users has attracted significant academic and industrial attention. Existing influence measurement techniques are vulnerable to sybil users that are thriving on Twitter. Although sybil defenses for online social networks have been extensively investigated, they commonly assume unique mappings from human-established trust relationships to online social associations and thus do not apply to Twitter where users can freely follow each other. This paper presents TrueTop, the first sybil-resilient system to measure the influence of Twitter users. TrueTop is rooted in two observations from real Twitter datasets. First, although non-sybil users may incautiously follow strangers, they tend to be more careful and selective in retweeting, replying to, and mentioning other users. Second, influential users usually get much more retweets, replies, and mentions than non-influential users. Detailed theoretical studies and synthetic simulations show that TrueTop can generate very accurate influence measurement results with strong resilience to sybil attacks.

This paper studies secret key establishment between two adjacent mobile nodes, which is crucial for securing emerging device-to-device (D2D) communication. As a promising method, cooperative key generation allows two mobile nodes to select some common neighbors as relays and directly extract a secret key from the wireless channels among them. A challenging issue that has been overlooked is that mobile nodes are often self-interested and reluctant to act as relays without adequate reward in return. We propose SYNERGY, a game-theoretical approach for stimulating cooperative key generation. The underlying idea of SYNERGY is to partition a group of mobile nodes into disjoint coalitions such that the nodes in each coalition fully collaborate on cooperative key generation. We formulate the group partitioning as a coalitional game and design centralized and also distributed protocols for obtaining the core solution to the game. The performance of SYNERGY is evaluated by extensive simulations.

Cooperative (spectrum) sensing is a key function for dynamic spectrum access and is essential for avoiding interference with licensed primary users and identifying spectrum holes. A promising approach for effective cooperative sensing over a large geographic region is to rely on special spectrum-sensing providers (SSPs), which outsource spectrum-sensing tasks to distributed mobile users. Its feasibility is deeply rooted in the ubiquitous penetration of mobile devices into everyday life. Crowdsourcing-based cooperative spectrum sensing is, however, vulnerable to malicious sensing data injection attack, in which a malicious CR users submit false sensing reports containing power measurements much larger (or smaller) than the true value to inflate (or deflate) the final average, in which case the SSP may falsely determine that the channel is busy (or vacant). In this paper, we propose a novel scheme to enable secure crowdsourcing-based cooperative spectrum sensing by jointly considering the instantaneous trustworthiness of mobile detectors in combination with their reputation scores during data fusion. Our scheme can enable robust cooperative sensing even if the malicious CR users are the majority. The efficacy and efficiency of our scheme have been confirmed by extensive simulation studies.

Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users. The first step toward effective PMSN is for mobile users to choose whom to interact with. Profile matching refers to two users comparing their personal profiles and is promising for user selection in PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers. This paper tackles this open challenge by designing novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The performance of our protocols is thoroughly analyzed and evaluated via real smartphone experiments.

Mobile Augmented Reality, as an emerging application for handheld devices, explores more natural interactions in real and virtual environments. For the purpose of accurate system response and manipulating objects in real-time, extensive efforts have been made to estimate six Degree-of-Freedom and extract robust feature to track. However there are still quite a lot challenges today in achieving rich user experience. To allow for a seamless transition from outdoor to indoor service, we investigated and integrated various sensing techniques of GPS, wireless, Inertial Measurement Units, and optical. A parallel tracking and matching scheme is presented to address the speed-accuracy tradeoff issue. Two prototypes, fine-scale mirror world navigation and context-aware trouble shooting, have been developed to demonstrate the suitability of our approach.