The lack of encryption and authentication mechanisms in USB standards renders USB traffic susceptible to sniffing attacks. This paper presents an initial effort to detect USB bus sniffing through the development of a detection system, SnoopDog. It does not require hardware redesign of USB devices or modifications to the kernel or USB protocol stack. The system utilizes a probe-and-detect strategy. The host PC generates bait traffic with a dummy endpoint address. While benign devices discard this traffic due to the address mismatch, a sniffer captures the data, consequently emitting responsive electromagnetic radiation (EMR). To determine whether a USB device is a sniffer, SnoopDog calculates the correlation between the bait traffic and the responsive EMR signals captured near the target device. A high correlation indicates the presence of a sniffer. Recognizing that sniffer's EMR signals can be weak, we introduce a novel temporal folding scheme to improve the signal-to-noise ratio (SNR). To evaluate the performance, we build a prototype of SnoopDog and conduct comprehensive evaluations under a variety of settings, where SnoopDog delivers a promising detection accuracy with minor system overhead.

The escalating threat of hidden GPS tracking devices poses significant risks to personal privacy and security.Featured by their miniaturization and misleading appearances, GPS devices can be easily disguised in their surroundings making their detection extremely challenging.In this paper, we propose a novel side-channel-driven detection system, GPSBuster, leveraging electromagnetic radiation (EMR) emitted by GPS trackers.Our feasibility studies and hardware analysis reveal that unique EMR patterns associated with the tracker's operation, stemming from the quartz oscillator, local oscillator, and mixer in the Mixed-Signal on Chip (MSoC) system.Nevertheless, as a side-channel leakage, EMRs can be extremely weak and suffer from the ambient noise interference, rendering the detection impractical.To address these challenges, we develop the signal processing techniques with noise removals and a dual-dimensional folding mechanism to accumulate the spectrum energy and protrude the EMR patterns with high Signal-to-Noise Ratios (SNR).Our detection prototype, built with a portable HackRF One device, allows users to perform a scan-to-detect manner and achieves an overall success rate of 98.4% on top-10 selling GPS trackers under various testing cases.The maximum detection range is 0.61m.

While video recommendation has been studied extensively in regular PC and smartphone settings, such a topic has been rarely discussed in the virtual reality (VR) context so far. On the other hand, as the popularity of VR videos continues to soar, its recommendation will play a crucial part in providing suggestions and guiding users through a deluge of available content. Given this unmet need, in this work, we present Bere, a video recommender system tailored for VR. Our approach leverages viewers' behavioral responses as they engage with VR videos to infer their preferences and thus make future recommendations. We integrate these new behavioral user-video interaction measures into the mainstream recommendation framework and renovate the graph learning-based paradigm to accommodate the new changes. The recommender system is further empowered with a novel domain adaptation approach named CMCCDA to address the data scarcity problem for model training. We also develop an energy-efficient adaptive encoding scheme to reduce the energy consumption on the VR device. We collect a behavioral dataset for video recommendation in VR and demonstrate through extensive evaluation that Bere significantly outperforms state-of-the-art schemes by up to 68.0% in precision and up to 28.8% in ranking quality.

Memory Electromagnetic Radiation (EMR) allows attackers to manipulate the DRAM of infiltrated systems to leak sensitive secret information. Although most of the existing works have demonstrated its feasibility, practical concerns, such as the ideal electromagnetic environment and stationary attacking layout, make the covert channel attack less convincing, especially in vulnerable sites such as offices and data centers. This work removes the above impractical assumptions to uncover the potential of memory EMR by proposing the first parallel EMR covert communication protocol. Our design reshapes the current "1-to-1" covert communication mode to "n-to-1" mode via a novel pattern-based 2-dimensional symbol encoding scheme, allowing multiple victim computers to simultaneously perform data exfiltration to one attacker (the receiver) without mutual interference. Meanwhile, this novel scheme design also enables the very first mobile attacker, i.e., a smartphone connected to a software-defined radio (SDR) dongle, to capture parallel memory EMR signals in a volatile environment. Extensive experiments are conducted to verify the performance in a volatile environment with different parameter configurations, distances, motion modes, shielding materials, orientations, hardware configurations, and SDR platforms. Our experimental results demonstrate that FreeEM can support up to 4 parallel memory EMR transmissions to achieve an overall throughput of 625Kbps and a decoding accuracy of 96.88%. The maximum communication distance can reach up to 20 meters.

Human faces have been widely adopted in many applications and systems requiring a high-security standard. Although face authentication is deemed to be mature nowadays, many existing works have demonstrated not only the privacy leakage of facial information but also the success of spoofing attacks on face biometrics. The critical reason behind this is the failure of liveness detection in biometrics. This work advances most biometric-based user authentication schemes by exploring dynamic biometrics (human facial activities) rather than traditional static biometrics (human faces). Inspired by observations from psychology, we propose the mmFaceID to leverage humans' dynamic facial activities when performing word reading for achieving robust, highly accurate, and effective user authentication via mmWave sensing. By addressing a series of technical challenges of capturing micro-level facial muscle movements using a mmWave sensor, we build a neural network to reconstruct facial activities via estimated expression parameters. Then, unique features can be extracted to enable robust user authentication regardless of relative distances and orientations. We conduct comprehensive experiments on 23 participants to evaluate mmFaceID in terms of distances/orientations, length of word lists, occlusion, and language backgrounds, demonstrating an authentication accuracy of 94.7%. We also extend our evaluation in a real IoT scenario. By speaking real IoT commends, the average authentication accuracy can reach up to 92.28%.

Most terminal devices authenticate users only once at the time of initial login, leaving the terminal unprotected during an active session when the original user leaves it unattended. To address this issue, continuous authentication has been proposed by automatically locking the terminal after a period of inactivity. However, it does not fully eliminate the risk of unauthorized access before the session expires. Recent research has also investigated the feasibility of using physiological and behavioral patterns as biometrics. This study presents a novel two-factor continuous authentication that explores a new form of signal called human-induced electric potential captured by wearables in contact with the user’s body. By analyzing this signal, we can determine the time of user-terminal interactions and compare it with information recorded by the terminal’s OS. If the original user remains on the same terminal, the two-source readings would match. Additionally, the proposed scheme includes an extra layer of protection by extracting terminal’s physical fingerprints from the human-induced electric potential to defend against advanced mimicry attacks. To test the effectiveness of our design, a low-cost wearable prototype is developed. Through extensive experiments, it is found that the proposed scheme has a low error rate of 2.3%, with minimal computational and energy requirements.

Data is becoming the world's most valuable asset and the ultimate renewable resource. This phenomenon has led to online personal data markets where data owners and collectors engage in the data sale and purchase. From the collector's standpoint, a key question is how to set a proper pricing rule that brings profitable tradings. One feasible solution is to set the price slightly above the owner's data cost. Nonetheless, data cost is generally unknown by the collector as being the owner's private information. To bridge this gap, we propose a novel learning algorithm, modified stochastic gradient descent (MSGD) that infers the owner's cost model from her interactions with the collector. To protect owners’ data privacy during trading, we employ the framework of local differential privacy (LDP) that allows owners to perturb their genuine data and trading behaviors. The vital challenge is how the collector can derive the accurate cost model from noisy knowledge gathered from owners. For this, MSGD relies on auxiliary parameters to correct biased gradients caused by noise. We formally prove that the proposed MSGD algorithm produces a sublinear regret of$\mathcal {O}(T^{\frac{5}{6}}\sqrt{\log (T^{\frac{1}{3}})})$. The effectiveness of our design is further validated via a series of in-person experiments that involve 30 volunteers.

Internet of Things (IoT) is transforming every corner of our daily life and plays important roles in the smart home. Depending on different requirements on wireless transmission, dedicated wireless protocols have been adopted on various types of IoT devices. Recent advances in Cross-Technology Communication (CTC) enable direct communication across those wireless protocols, which will greatly improve the spectrum utilization efficiency. However, it incurs serious security concerns on heterogeneous IoT devices. In this paper, we identify a new physical-layer attack, cross-technology signal emulation attack, where a WiFi device eavesdrops a ZigBee packet on the fly, and further manipulates the ZigBee device by emulating a ZigBee signal. To defend against this attack, we propose two defense strategies with the help of a commonly found WiFi router. Particularly, the passive defense strategy focuses on misleading the ZigBee signal eavesdropping, while the proactive approach develops a real-time detection mechanism on distinguishing between a common ZigBee signal and an emulated signal. We implement the complete attacking process and defense strategies with TI CC26x2R LaunchPad, USRP-N210 platform, and a self-designed prototype. Extensive experiments have demonstrated the existence of the attack, and the feasibility, effectiveness, and accuracy of the proposed defense strategies.

Mobile crowdsourcing has emerged as a promising paradigm that applies the principle of crowdsourcing to perform tasks of mobility requirement. Due to the openness of mobile crowdsourcing, workers may yield low-quality task answers. To alleviate this problem, substantial efforts have been devoted to elicit truthful data from workers. On the other hand, to facilitate task assignment, workers are required to upload the platform their profiles, such as locations and expertise. Therefore, task assignment outcomes and thus mobile crowdsourcing service accuracy is subject to the quality of workers’ self-reported profiles. In this paper, we leverage incentive design to motivate workers to honestly reveal both task answers and their profiles. The challenge is to design one incentive payment for truth elicitation in two kinds of submissions. For this, we first derive the sufficient and necessary conditions for answer truthfulness and profile truthfulness separately. We then construct an incentive optimization problem that incorporates these conditions as constraints. Its optimal solution lists the payment to each worker that elicits answers and profiles jointly. Our proposed mechanism, with a formally proved bounded approximation ratio, ensures that truth-telling is a Bayesian Nash equilibrium. We prototype the mechanism and conduct a series of experiments that involve 30 volunteers to validate the efficacy and efficiency of the proposed mechanism.

In the wake of the Web 2.0, crowdsourcing has emerged as a promising approach to maintain a flexible workforce for human intelligence tasks. To stimulate worker participation, many reverse auction-based incentive mechanisms have been proposed. Designing auctions that discourage workers from cheating and instead encouraging them to reveal their true cost information has drawn significant attention. However, the existing efforts have been focusing on tackling individual cheating misbehaviors, while the scenarios that workers strategically form collusion coalitions and rig their bids together to manipulate auction outcomes have received little attention. To fill this gap, in this work we develop a$(t,p)$-collusion resistant scheme that ensures no coalition ofweighted cardinality$t$can improve its group utility by coordinating the bids at a probability of$p$. This paper takes into account the unique features of crowdsourcing, such as diverse worker types and reputations, in the design. The proposed scheme can suppress a broad spectrum of collusion strategies. Besides, desirable properties, including$p$-truthfulness and$p$-individual rationality, are also achieved. To provide a comprehensive evaluation, we first analytically prove our scheme's collusion resistance and then experimentally verify our analytical conclusion using a real-world dataset. Our experimental results show that the baseline scheme, where none of the critical properties is guaranteed, costs up to 20.1 times the optimal payment in an ideal case where no collusion exists, while our final scheme is merely 4.9 times the optimal payment.

Quality of Experience (QoE) assessment is a long-lasting but yet-to-be-resolved task. Existing approaches, especially for conversational voice services, are restricted to leveraging network-centric parameters. However, their performances are hardly satisfactory due to the failure to consider comprehensive QoE-related factors. Moreover, they develop a one-for-all model that is uniform for all individuals and thus incapable of handling user diversity in QoE perception. This paper proposes a personalized QoE assessment model, namely SpeechQoE. It exploits speaker's speech signals to infer individual's perceived quality in voice services. SpeechQoE fundamentally addresses the drawback of conventional models. Instead of enumerating and incorporating unlimited QoE-related factors, SpeechQoE takes as input speech signals that inherently bear rich information needed for QoE assessment of the speaker. SpeechQoE employs an efficient few-shot learning framework to adapt the model to a new user quickly. We additionally design a lightweight data synthetic scheme to minimize the overhead of data collection needed for model adaption. A modular integration with a conventional parametric model is further implemented to avoid issues caused by the clean-slate data-driven approach. Our experiments show that SpeechQoE achieves an accuracy of 91.4% in QoE assessment which outperforms the state-of-the-art solutions by a clear margin. As another contribution of this work, we build a dataset that would be the first source of annotated audio tracks for QoE assessment of conversational calls.

The increasing deployment of wireless sensors enables a broad spectrum of health-related wearable applications. Due to the sensitivity of collected personal health information, these wearables should be authenticated together with their users as “wearable-user pairs” to ensure that they are attached to legitimate users. However, various devices are equipped with dedicated sensing abilities and wireless protocols corresponding to data characteristics in practice. Traditional authentication methodologies may not work in this heterogeneous environment because of protocol incompatibility. For example, how to verify a new ZigBee-enabled monitor when the existing trusted device is Wi-Fi-enabled? Therefore, to achieve authentication across protocols, in this article, we leverage the unique cross-technology interference (CTI), triggered by heterogeneous wireless transmissions, along with human physiological activity measurements (e.g., respiration patterns) to design an authentication scheme between wearables and users. Specifically, the authentication from an unknown ZigBee wearable to a trusted Wi-Fi device is achieved by monitoring the channel state information (CSI) changes according to human respiration. Our approach not only successfully recognizes a legitimate wearable-user pair but also blocks illegal access from adversaries. Extensive experiments have been conducted to demonstrate both the security and feasibility of the proposed scheme. The designed mechanism can achieve over 92% authentication accuracy with human subjects.

The ubiquitous mobile portable devices have accelerated the rise of mobile crowdsensing (MCS), a distributed perception paradigm. In MCS, multiple requesters issue their sensing tasks on a server platform, and then the platform distributes the tasks to multiple workers. In this process, requesters typically specify the tasks and requirements; meanwhile, the needed task pricing to workers is also specified, which is used to offsetting worker’s efforts by completing the tasks. However, for different application scenarios, the task requirements, the time periods, and the resource consumptions for completing the tasks are varied, which results in a challenge to raise an appropriate task pricing to diverse workers. Therefore, we study the dynamic task pricing problem in the MCS network system with diverse factors (e.g., multiple requester queueing competitions, dynamic task requirements, and distinct waiting time costs). To solve the problem, we resort to the theory of Age of Information (AoI). Specifically, we leverage the AoI timeliness metric in modeling the requester’s waiting time costs, and then we use queueing game theory to build the dynamic task pricing model. The model analysis has shown the existence of the optimal pricing strategies under the first-come–first-served (FCFS) queueing rule and the last-come–first-served with preemptive in waiting (LCFSW) queueing rule. Finally, numerical simulations are conducted to validate the existence of the optimal task pricing.

Nonlinearly constrained nonlinear programming (NLC-NLP) problems arise in various real-world decision-making fields, such as financial engineering, urban planning, supply chain management, and power system control. They are usually large-scale because of having to consider massive variables and constraints. Solving NLC-NLP problems by employing common algorithms (e.g., gradient projection method (GPM)) is usually computationally-expensive, which challenges common organizations in solving large-scale NLC-NLP problems. To address this issue, an option is to adopt cloud computing for help. However, this raises security concerns since real-world NLC-NLP problems may carry sensitive information. Although previous secure outsourcing algorithms try to protect sensitive information, they still let cloud service tenants bear heavy computation burden. In this paper, we develop a practical secure outsourcing algorithm for using the GPM to solve large-scale NLC-NLP problems. To be more prominent, to accelerate computations and avoid possible memory overflowing, we parallelize the developed algorithm. We implement the developed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop, and also offer extensive experiment results to show that the developed algorithm can reduce the tenant’s computing time significantly.

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages the power of ubiquitous mobile devices to acquire rich information regarding their surrounding environment. In many location-based sensing tasks, workers are required to associate their sensing reports with corresponding geographic coordinates. Such information leaves a trail of worker's historical location record which thus poses a severe threat to their location privacy. On the other hand, individual workers may perceive location privacy differently. Instead of following conventional solutions that aim to perfectly hide user privacy, this paper adopts a novel alternative approach. Auser-centriclocationprivacytrading framework, called ULPT, is constructed to facilitate location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform in an MCS task based on its own location privacy leakage budget$\xi$. The higher$\xi$is, the more privacy its reported location discloses. Accordingly, it receives higher payment from the platform as compensation. Besides, ULPT enables the platform to select a suitable set of winning workers to achieve desirable MCS service accuracy while taking into account of its budget limit and worker privacy requirements. For this purpose, a heuristic algorithm is devised with a bounded optimality gap. As formally proved in this manuscript, ULPT guarantees a series of nice properties, including$\xi$-privacy,$(\alpha, \beta)$-accuracy,budget feasibility. Moreover, both rigorous theoretical analysis and extensive simulations are conducted to evaluate tradeoffs among these three.

Cyber-physical systems revolutionize how we interact with physical systems. Smart grid is a prominent example. With new features such as fine-grained billing, user privacy is at a greater risk than before. For instance, a utility company () can infer users’ (fine-grained) usage patterns from their payment. The literature only focuses on hiding individual meter readings in bill calculation. It is unclear how to preserve amount privacy when the needs to assert that each user has settled the amount as calculated in the bill.

This study presents Periscope, a novel side-channel attack that exploits human-coupled electromagnetic (EM) emanations from touchscreens to infer sensitive inputs on a mobile device. Periscope is motivated by the observation that finger movement over the touchscreen leads to time-varying coupling between these two. Consequently, it impacts the screen's EM emanations that can be picked up by a remote sensory device. We intend to map between EM measurements and finger movements to recover the inputs. As the significant technical contribution of this work, we build an analytic model that outputs finger movement trajectories based on given EM readings. Our approach does not need a large amount of labeled dataset for offline model training, but instead a couple of samples to parameterize the user-specific analytic model. We implement Periscope with simple electronic components and conduct a suite of experiments to validate this attack's impact. Experimental results show that Periscope achieves a recovery rate over 6-digit PINs of 56.2% from a distance of 90 cm. Periscope is robust against environment dynamics and can well adapt to different device models and setting contexts.

Blockchain, an emerging decentralized but trusted system, has been applied in many applications, such as the Internet of Things (IoT), supply chains, and smart grid. However, due to the large amount of computing and storage resources blockchain typically demands, its wide deployment is faced with the sustainability issue. To resolve this issue, a viable solution is to empower the IoT system with fog computing that can offload the computation-demanding tasks. Due to varieties of mining tasks and heterogeneous resource capabilities at fog nodes (FNs), it is not an easy task to schedule mining tasks and manages resource allocation among FNs of conflicting interests and independent IoT devices in a distributed manner. In this article, under the framework of matching theory, we design a distributed matching mechanism to maximize the social welfare of resource-restricted FNs while guaranteeing various mining requirements of FNs. Besides, we also provide formal proof regarding the convergence and computational complexity of a distributed matching algorithm (DMA). Finally, we verify that DMA not only improves the social welfare of FNs but also reduces the mining latency compared with the existing algorithms through extensive simulations.

Spectrum trading benefits both secondary users (SUs) and primary users (PUs), while it poses great challenges to maximize PUs' revenue, since SUs' demands are uncertain and individual SU's traffic portfolio contains private information. In this paper, we propose a data-driven spectrum trading scheme which maximizes PUs' revenue and preserves SUs' demand differential privacy. Briefly, we introduce a novel network architecture consisting of the primary service provider (PSP), the secondary service provider (SSP) and the secondary traffic estimator and database (STED). Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs' demands. To estimate SUs' demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs' demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs' demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.

Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers' properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers' locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers' locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.

While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and attract much attention, they have been approved as an extremely challenging task. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Most existing methods, on the other hand, model traffic patterns as probabilistic distributions or stochastic processes and impose stringent assumptions over these models. Such assumptions may be beneficial at providing closed-form formula in evaluating prediction performances, but fall short for practice use. In this paper we propose STEP, aspatio-temporal fine-granular user trafficprediction mechanism for cellular networks. A deep graph convolution network, called GCGRN, is constructed. It is a novel combination of the graph convolution network (GCN) and gated recurrent units (GRU), which exploits graph neural network to learn an efficient spatio-temporal model from a user’s massive dataset for traffic prediction. The prototype of STEP has been implemented. Extensive experimental results demonstrate that our model outperforms the state-of-the-art time-series based approaches. Besides, STEP merely incurs mild energy consumption, communication overhead and system resource occupancy to mobile devices. Moreover, NS-3 based simulations validate the efficacy of STEP in reducing session dropping ratio in cellular networks.

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers' privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Mobile edge computing is becoming a major trend in providing computation capacities at the edge of mobile networks. Meanwhile, unmanned aerial vehicles (UAVs) have been considered as distinctly important integrated components to extend services coverage. In order to provide users with higher and satisfied quality of services, edge computing resources need to be allocated between edge computing stations (ECSs) and UAVs in mobile networks. However, there are significant security and privacy problems due to the open environments of ECSs and UAVs. In this paper, we propose a resource pricing and trading scheme based on Stackelberg dynamic game to optimally allocate edge computing resources between ECSs and UAVs, and blockchain technology is applied to record the entire resources trading process to protect the security and privacy. The ECSs control the resources price of the allocated edge computing resources, where the UAVs follow the price announced by the ECSs and make optimal decisions on the edge computing resources demands. Blockchain is integrated in the resource trading process to ensure the security and privacy. Numerical simulations are given to show the effectiveness of the proposed scheme.

Wearable devices that capture user's rich information regarding their health conditions and daily activities have unmet pairing needs. Today's solutions, which primarily rely on human involvement, are cumbersome, error-prone, and do not scale well. Despite some prior efforts trying to fill this gap, they either rely on some sophisticated sensors, such as electromyogram (EMG) or electrocardiogram (ECG) pads that may not universally exist, or non-trivial design of communication transceivers that cannot be found easily on current commercial devices. Therefore, a pairing scheme for wearable devices that is secure, practical, and convenient is in dire need. In this paper, we propose a novel approach that leverages ambient radio frequency (RF) noise. Our design is based on a key observation that received RF noise power measured in the logarithmic scale at different parts of a human body surface experience the same variation trend, whereas those from different human bodies or off the body are distinct. Wearables make use of the observed noise as the entropy source for the proposed pairing protocol. Extensive experiments show that our scheme has an equal error rate (EER) as low as 1.4% for pairing. Its key generation rate reaches 138 bits/sec, which beats so-far existing pairing schemes. Besides, our scheme can be efficiently executed within 0.97 s. Its incurred energy consumption is as low as 0.27 J for the entire pairing procedure.

The auction mechanism is deemed to be an effective method to address the problem of spectrum scarcity. Numerous spectrum auction mechanisms can alleviate spectrum shortage under the consideration of truthfulness, social welfare maximization and spectrum reusability, while the privacy preservation and preferences of primary/secondary users have not been fully discussed. In this paper, we propose a matching based double auction mechanism for spectrum trading with differential privacy (MastDP) to protect the privacy of buyers/sellers from the untrustworthy auctioneer, other buyers/sellers and other potential parties. Each participant adds distributed differential private noise following Geom(α) distribution to his bid value and encrypts the noisy bid value. The auctioneer can decrypt only the sum of all uploaded noisy bid values and determines the clearing price by using its private key. Based on the clearing price, the matching theory is adopted to maximize the winning participants' revenue while fully considering their preferences and spectrum reuse. Simulation results show that MastDP achieves satisfactory performance in terms of economic properties' privacy preservation and spectrum trading efficiency.

Mobile crowd sensing (MCS) is a technique where sensing tasks are outsourced to a crowd of mobile users. Since most of sensing tasks are location-dependent, workers are required to embed their locations into sensing reports, which incurs location privacy vulnerabilities. Realizing that workers perceive their location privacy differently, in this work we construct an auction-based trading market, facilitating location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform based on its own location privacy leakage budget $\xi$. The higher $\xi$ is, the less secrecy its reported location preserves. As a result, it receives higher payment from the platform as a compensation to its privacy loss. Besides, our mechanism enables the platform to select a suitable set of winning workers to achieve desirable service accuracy. For this purpose, a heuristic algorithm is devised, with polynomial-time complexity and bounded optimality gap. As formally proved in this manuscript, our proposed mechanism guarantees a series of nice properties, including $\xi$-privacy, $(\alpha,\beta)$accuracy, and budget feasibility.

In current healthcare systems, patients use various types of medical Internet of Things devices for monitoring their health conditions. The collected information (personal health records) will be sent back to hospitals for diagnosis and quick responses. However, severe security and privacy leakages with regard to data privacy and identity authentication are incurred because the monitored health data contains sensitive information. Therefore, the data should be well protected from unauthorized entities. Unfortunately, traditional cryptographic approaches or password-based mechanisms cannot fulfill the privacy and security demands in health monitoring due to their low efficiency and knowledge-based property. Biometric authentication overcomes these deficiencies and successfully verifies the inherent characteristics of humans. Among all biometrics, the electrocardiogram (ECG) signal is the most suitable one due to its medical properties. However, the security and privacy objectives of ECG-based authentication usually fail in practice due to the noise interferences in the collected ECG data and the privacy breach of the ECG database. In this paper, we propose a practical scheme that can reliably authenticate patients with noisy ECG signals and provide differentially private protection simultaneously. The effectiveness and efficiency of our scheme are thoroughly analyzed and evaluated over online datasets. We also conduct a pilot study on human subjects experiencing different exercise levels to validate our scheme.

Wearable devices that capture users' rich information regarding their daily activities have unmet authentication needs. Today's solutions, which primarily rely on indirect authentication mechanisms via users' smartphones, thus cumbersome and susceptible to adversary intrusions. Even though there have been some efforts trying to fill this gap, they either rely on some superior sensors, such as cameras and electrocardiogram (ECG) pads, or are awkward to use, e.g., users are asked to perform some pre-defined movement/gesture for authentication. Therefore, an authentication mechanism for wearable devices that is accurate, robust, light-weight and convenient is in dire need.

Manufacturing systems are rapidly adopting the Internet of Things (IoT) to improve their efficiency and productivity. The IoT equips manufacturing systems with sensing, computing and communications capabilities, which enable real-time monitoring and control of increasingly complex and geographically distributed factory floors. However, the increased used of computer networks in IoT-enabled manufacturing introduces cyber-vulnerabilities that can be exploited by sophisticated adversaries to sabotage manufacturing operations. A particularly serious cyberattack against manufacturing systems is the defect injection (DI) attack. In a DI attack, a compromised machine fabricates objects with deformed geometry, weak material composition, abnormal dimensions, etc., which pose a great risk to safety-critical applications. In this paper, we develop a method to identify compromised machines that launch DI attacks against smart manufacturing systems. Specifically, we first propose a DI attack localization (DIAL) algorithm that uses machines' energy consumption and voltage measurements to identify compromised machines in the system. Our proposed approach only requires modest hardware resources and can be used in large-scale systems. We implement our DIAL algorithm on a real-world advanced manufacturing testbed, and observe that it can successfully locate the compromised machines with a high detection rate.

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which aims to address the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers' data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve a desirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, we explore the problem structure, characterize the hidden monotonicity property of the problem, and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can recruit a set of workers to approximately minimize the cost of purchasing private sensing data from workers subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Device-to-device (D2D) communications can potentially alleviate cellular network congestion by utilizing local available links, and have attracted intensive attention recently. Cognitive radio (CR) allows users to opportunistically access unused licensed spectrums. It thus serves as a great candidate technology for D2D communications, but has not been widely employed in cellular networks due to hardware development limitations. In this paper, we propose a new architecture, called cognitive mesh assisted cellular network (CMCN), in which several secondary service providers (SSPs) deploy CR routers to facilitate D2D communications among wireless users. To address the competition among the SSPs, we further construct a secondary spectrum auction market. Although a few works have studied spectrum auctions, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Uncertain spectrum availability is not considered in previous schemes either. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can address these problems. Extensive simulations are conducted to validate the efficiency of the CMCN architecture and that of the TOA scheme.

The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps. However, the ever-increasing data traffic has exacerbated the congestion on current cellular networks, which results in users' dissatisfaction, especially in crowded areas. Hence, how to alleviate data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic originally targeted to cellular networks, such as the small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of users and the social features among a group of users. A mobile caching user, who pre-caches a certain amount of contents, will roam around congested areas to participate in content dissemination in order to satisfy users' requests, which is expected to benefit both himself and users in the crowd simultaneously. To motivate such human-enabled mobile participation for data offloading, a Stackelberg game is deployed with joint considerations on social effect and delay effect. Based on detailed performance analysis, we demonstrate the feasibility and efficiency of the proposed approach.

Caching content at the edge of the network is a promising way to improve content delivery efficiency. In most existed research, content caching strategies are typically designed to maximize local hit rates, improve energy efficiency or reduce network cost. However, this metric cannot guarantee the utility of content providers (CPs). To encounter with this challenge, we construct a hierarchical content distribution problem, within this which, two layers are included called caching selection and content delivery, respectively. The former layer intends to find content caching places, i.e. service providers(SPs) for contents while guaranteeing CP's utility, and the latter layer utilizes multi-seller multi-buyer multi- content trading auction (MMMTA) to characterize the competition between the SPs and users. To solve the proposed problem, a hierarchical content distribution iteration (HCDI) method is designed. Various simulation results show the property of the proposed scheme.

Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV's video stream was compromised. Since UAVs extensively utilize autonomous behavior, it is important to develop an autopilot system that is robust to potential cyber-attack. In this work, we present a biometric system to encrypt communication between a UAV and a computerized base station. This is accomplished by generating a key derived from the Beta component of a user's EEG. When communication with a UAV is attacked, a safety mechanism directs the UAV to a safe ‘home’ location. This system has been validated on a commercial UAV under malicious attack conditions.

Electric power systems are critical infrastructure and are vulnerable to contingencies including natural disasters, system errors, malicious attacks, etc. These contingencies can affect the world's economy and cause great inconvenience to our daily lives. Therefore, security of power systems has received enormous attention for decades. Recently, the development of the Internet of Things (IoT) enables power systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy with IoT devices (such as sensors, smart meters, etc.). On the other hand, it also incurs many more security threats. Cascading failures, one of the most serious problems in power systems, can result in catastrophic impacts such as massive blackouts. More importantly, it can be taken advantage by malicious attackers to launch physical or cyber attacks on the power system. In this paper, we propose and investigate cascading failure attacks (CFAs) from a stochastic game perspective. In particular, we formulate a zerosum stochastic attack/defense game for CFAs while considering the attack/defense costs, budget constraints, diverse load shedding costs, and dynamic states in the system. Then, we develop a Q-CFA learning algorithm that works efficiently in power systems without any a priori information. We also formally prove that the convergence of the proposed algorithm achieves a Nash equilibrium. Simulation results validate the efficacy and efficiency of the proposed scheme by comparisons with other state-of-the-art approaches.

The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester (task owner) can crowdsource data from the workers (smartphone users) by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in terms of limited computation and storage resources, cloud-assisted approaches may serve as a promising way to tackle the big data analysis issue. However, workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrusted cloud. In this paper, we propose to the use cloud to compute a set operation for the requester, at the same time workers' data privacy and identities privacy are well preserved. Besides, the requester can verify the correctness of set operation results. We also extend our scheme to support data preprocessing, with which invalid data can be excluded before data analysis. By using batch verification and data update methods, the proposed scheme greatly reduces the computational cost. Extensive performance analysis and experiment based on real cloud system have shown both the feasibility and efficiency of our proposed scheme.

Crowdsensing recently attracts great attention from both industry and academia. By fusing and analyzing multi- dimensional sensing data collected from crowdsensing users, it is possible to support health caring, environment mentoring, traffic mentoring and social behavior mentoring. Nonetheless, how to preserve users' data privacy during data fusing, e.g., data aggregation, has been rarely discussed for crowdsensing before. Besides, due to the dynamics of sensing environments and available resources at users, there will be missing elements from users' sensing results. In this paper we aim to achieve privacy-preserving data aggregation over incomplete data for crowdsensing. A novel scheme is developed based on linear transformation and homomorphic encryption scheme. It enables the server to obtain aggregation results over recovered sensing results without learning their individual details. Security analysis and performance evaluation are conducted showing the effectiveness and efficiency of our scheme.

The database-driven cognitive radio network (CRN) is regarded as a promising way for a better utilization of spectrum resources without introducing the interference to primary users (PUs). However, there are some critical security and privacy issues in database-driven CRNs, which have been rarely discussed before. First of all, in order to retrieve the spectrum available information (SAI) of one's vicinity, an SU's query will inevitably disclose its location information. Second, malicious SUs may query SAI for other locations so as to infer operational patterns of PUs and other SUs. In addition, they can reconstruct the entire SAI of the database and sell it for profit. Therefore, in this paper we aim to guarantee both location privacy of SUs and information security of the database during spectrum query in database-driven CRNs. We first leverage private information retrieval (PIR) techniques to allow the database to find out the SAI regarding a querying SU's location, without learning the query information, i.e., this SU's location. To prevent malicious SUs inferring SAI of other locations, SUs are required to provide location proofs indicating that they are at the places where they claim to be. Theoretical analysis is provided showing that our scheme is privacy-preserving and secure. Experiments are also conducted to evaluate the its efficiency.

The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps, such as social networking, mobile video services, and location-based services, etc. However, the ever-increasing data traffic has exacerbated congestions on current cellular networks, which results in users' dissatisfaction, especially in crowded areas. Hence, how to deal with the explosive data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic targeted to cellular networks, such as small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users will still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of mobile users and the social features among a group of users. A mobile caching user, who pre- caches certain amount of contents, can roam around congested areas to participate in data dissemination in order to satisfy users' requests, which can benefit both herself and users in the crowd simultaneously. Therefore, we propose a game theoretical approach to analyze the data offloading via mobile participation with joint considerations on network effects, congestion, social behaviors, and pricing strategy. Based on detailed performance analysis, we show the feasibility and efficiency of the proposed approach.

Crowdsourcing is a crowd-based outsourcing, where a requester (task owner) can outsource tasks to workers (public crowd). Recently, mobile crowdsourcing, which can leverage workers' data from smartphones for data aggregation and analysis, has attracted much attention. However, when the data volume is getting large, it becomes a difficult problem for a requester to aggregate and analyze the incoming data, especially when the requester is an ordinary smartphone user or a start-up company with limited storage and computation resources. Besides, workers are concerned about their identity and data privacy. To tackle these issues, we introduce a three-party architecture for mobile crowdsourcing, where the cloud is implemented between workers and requesters to ease the storage and computation burden of the resource-limited requester. Identity privacy and data privacy are also achieved. With our scheme, a requester is able to verify the correctness of computation results from the cloud. We also provide several aggregated statistics in our work, together with efficient data update methods. Extensive simulation shows both the feasibility and efficiency of our proposed solution.

The security and privacy threats on e-commerce have attracted intensive attention recently. The explosive growth of online social networks (OSNs) has made them potential new great marketplaces for e-commerce, which, however, raise serious security and privacyconcerns. This is mainly due to the centralized system architecture where the service provider knows all users’ private data and becomes the single point of failure. To this end, we propose a secure and private auction framework, called SPA, for decentralized online social networks (DOSNs). SPA consists of three phases: identity initiation, buyer-seller matching, and private auction. It requires no trust among the participants but can provide security, privacy, authenticity, non-repudiation, and correctness for the auctions. We analyze the computation and communication complexities of the proposed private auction scheme, which are$O(n+K)$for each node where$n$is the number of bidders and$K$is the number of pricing points. In contrast, those of previous auction schemes are$O(nK)$at best. The storage complexity is significantly lower than before as well. Security and privacy of SPA are also analyzed. Extensive experiments are conducted to validate the efficiency of SPA.

The prevalence of smartphones with geo-positioning functionalities gives rise to a variety of location-based services (LBSs). Proximity test, an important branch of location-based services, enables the LBS users to determine whether they are in a close proximity with their friends, which can be extended to numerous applications in location-based mobile social networks. Unfortunately, serious security and privacy issues may occur in the current solutions to proximity test. On the one hand, users' private location information is usually revealed to the LBS server and other users, which may lead to physical attacks to users. On the other hand, the correctness of proximity test computation results from LBS server cannot be verified in the existing schemes and thus the creditability of LBS is greatly reduced. Besides, privacy should be defined by user him/herself, not the LBS server. In this paper, we propose a privacy-preserving verifiable proximity test for location-based services. Our scheme enables LBS users to verify the correctness of proximity test results from LBS server without revealing their location information. We show the security, efficiency, and feasibility of our proposed scheme through detailed performance evaluation.

Many truthful spectrum auction schemes have been recently proposed to to ensure that the dominant strategy for bidders is to bid truthfully and thus protect the auctioneer's benefits. However, most of them assume the auctioneer is trustful and do not protect bidders' interests. An auctioneer can manipulate the winner's charging price if it knows bidders' bids. Thus, it is critical to protect bids from the auctioneer. Towards this end, we develop a Privacy-Preserving Economic-Robust spectrum auction scheme, namely PPER. Not only does it well protect users' bid privacy, but also guarantees economic-robustness which is another important auction property. Besides, only transmitters but not receivers are considered in most previous spectrum auctions, resulting in many unexpected collisions during transmissions. In this work, we consider interference constraints from transmissions instead of transmitters in spectrum allocation. Extensive privacy analysis and simulation results show the effectiveness and efficiency of our scheme.

Widely deployed mHealth systems enable patients to efficiently collect, aggregate, and report their Personal Health Records (PHRs), and then lower the costs and shorten their response time. The increasing needs of PHR monitoring require the involvement of healthcare companies that provide monitoring programs for analyzing PHRs. Unfortunately, healthcare companies are lack of the computation, storage, and communication capability on supporting millions of patients. To tackle this problem, they seek for the help from the cloud. However, delegating monitoring programs to the cloud may incur serious security and privacy breaches because people have to provide their identity information and PHRs to the public domain. Even worse, the cloud may mistakenly return the incorrect computation results, which will put patients' life in jeopardy. In this paper, we propose a verifiable privacy-preserving monitoring scheme for cloud-assisted mHealth systems. Our scheme allows patients to verify the correctness of computation results from the cloud without revealing their PHRs and identity information. In addition, our advanced schemes offer efficient PHR updates and PHR computations on complex monitoring programs. By detailed performance evaluation, we have shown the security and efficiency of our proposed scheme.

Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC2N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a non-linear programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ϵ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ϵ) and larger than (1 - ϵ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ϵ-bounded approximate results and the optimal result as well.

Due to the emerging various data services, current cellular networks have been experiencing a surge of data traffic and are already overloaded; thus, they are not able to meet the ever exploding traffic demand. In this study, we first introduce a multi-radio multi-channel multi-hop cognitive cellular network (M$^3$C$^2$N) architecture to enhance network throughput. Under the proposed architecture, we then investigate the minimum length scheduling problem by exploring joint frequency allocation, link scheduling, and routing. In particular, we first formulate a maximal independent set based joint scheduling and routing optimization problem called original optimization problem (OOP). It is a mixed integer non-linear programming (MINLP) and generally NP-hard problem. Then, employing a column generation based approach, we develop an$\epsilon$-bounded approximation algorithm which can obtain an$\epsilon$-bounded approximate result of OOP. Noticeably, in fact we do not need to find the maximal independent sets in the proposed algorithm, which are usually assumed to be given in previous works although finding all of them is NP-complete. We also revisit the minimum length scheduling problem by considering uncertain channel availability. Simulation results show that we can efficiently find the$\epsilon$-bounded approximate results and the optimal result as well, i.e., when$\epsilon =0\%$in the algorithm.

In ad hoc networks, selfish nodes deviating from the standard MAC (Medium Access Control) protocol can significantly degrade normal nodes' performance and are usually difficult to detect. In this paper, we propose detection and defense schemes to identify and defend against MAC-layer selfish misbehavior, respectively, in IEEE 802.11 multi-hop ad hoc networks. Specifically, the non-deterministic nature of the IEEE 802.11 MAC protocol imposes great challenges to distinguishing selfish nodes from well-behaved nodes. Most traditional selfish misbehavior detection approaches are for wireless local area networks (WLANs) only. They either rely on a large amount of historical data to perform statistical detection, or employ throughput or delay models that are only valid in WLANs for detection. In contrast, we propose a realtime selfish misbehavior detection scheme for multi-hop ad hoc networks. It requires only several samples, and hence is more efficient and can adapt to channel dynamics more quickly. Then, based on the proposed detection scheme, we design three selfish misbehavior defense schemes against three typical kinds of smart selfish nodes. We find that the smart selfish nodes cannot degrade normal nodes' performance much without getting detected. Extensive simulation results are finally presented to validate the proposed detection and defense schemes.

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. Some works propose to let people become friends if they have similar profile attributes. However, profile matching involves an inherent privacy risk of exposing private profile information to strangers in the cyberspace. The existing solutions to the problem attempt to protect users’ privacy by privately computing the intersection or intersection cardinality of the profile attribute sets of two users. These schemes have some limitations and can still reveal users’ privacy. In this paper, we leverage community structures to redefine the OSN model and propose a realistic asymmetric social proximity measure between two users. Then, based on the proposed asymmetric social proximity, we design three private matching protocols, which provide different privacy levels and can protect users’ privacy better than the previous works. We also analyze the computation and communication cost of these protocols. Finally, we validate our proposed asymmetric proximity measure using real social network data and conduct extensive simulations to evaluate the performance of the proposed protocols in terms of computation cost, communication cost, total running time, and energy consumption. The results show the efficacy of our proposed proximity measure and better performance of our protocols over the state-of-the-art protocols.

With the ever increasing user adoption of mobile devices like smart phones and tablets, the cellular service providers' energy consumption and cost are fast-growing and have received tremendous attention. How to effectively reduce the energy cost of cellular networks and achieve green communications while satisfying cellular users' rocketing traffic demands has become an urgent and challenging problem. In this paper, we investigate the minimization of the long-term time-averaged expected energy cost of a cellular service provider while guaranteeing the strong stability of the network. We first formulate an offline optimization problem with a joint consideration of flow routing, link scheduling, and energy (i.e., renewable energy resource, energy storage unit, etc.) constraints. Since the formulated problem is a time-coupling stochastic Mixed-Integer Non-Linear Programming (MINLP) problem, it is prohibitively expensive to solve. Then, we reformulate the problem by employing Lyapunov optimization theory. A decomposition based algorithm is developed to solve the problem, which is proved to guarantee the network strong stability. Both the lower and upper bounds on the optimal result of the original problem are derived and proven. Simulation results demonstrate that the obtained lower and upper bounds are very tight, and that the proposed scheme results in noticeable energy cost savings.

The proliferation of mobile devices has driven the mobile marketing to surge in the past few years. Emerging as a new type of mobile marketing, mobile location-based services (MLBSs) have attracted intense attention recently. Unfortunately, current MLBSs have a lot of limitations and raise many concerns, especially about system security and users' privacy. In this paper, we propose a new location-based rewarding system, called LocaWard, where mobile users can collect location-based tokens from token distributors, and then redeem their gathered tokens at token collectors for beneficial rewards. Tokens act as virtual currency. The token distributors and collectors can be any commercial entities or merchants that wish to attract customers through such a promotion system, such as stores, restaurants, and car rental companies. We develop a security and privacy aware location-based rewarding protocol for the LocaWard system, and prove the completeness and soundness of the protocol. Moreover, we show that the system is resilient to various attacks and mobile users' privacy can be well protected in the meantime. We finally implement the system and conduct extensive experiments to validate the system efficiency in terms of computation, communication, energy consumption, and storage costs.

The rapid growth of wireless devices and services exacerbates the problem of spectrum scarcity in wireless networks. Recently, spectrum auction has emerged as one of the most promising techniques to enhance spectrum utilization and mitigate this problem. Although there exist some works studying spectrum auction, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Moreover, most previous auction schemes only focus on satisfying the incentive compatibility property, also called truthfulness, but ignore another two critical properties: individual rationality, and budget balance. Thus, they may not be economic-robust. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can support multi-hop data traffic, ensure economic-robustness, and generate high revenue for the auctioneer. Specifically, in TOA, instead of spectrum bands as in traditional spectrum auction schemes, users bid for transmission opportunities (TOs). A TO is defined as the permit of data transmission on a specific link using a certain band, i.e., a link-band pair. The TOA scheme is composed of three procedures: TO allocation, TO scheduling, and pricing, which are performed sequentially and iteratively until the aforementioned goals are reached. We prove that TOA is economic-robust, and conduct extensive simulations to show its effectiveness and efficiency.

With great advances in mobile devices, e.g., smart phones and tablets, location-based services (LBSs) have recently emerged as a very popular application in mobile networks. However, since LBS service providers require users to report their location information, how to preserve users' location privacy is one of the most challenging problems in LBSs. Most existing approaches either cannot fully protect users' location privacy, or cannot provide accurate LBSs. Many of them also need the help of a trusted third-party, which may not always be available. In this paper, we propose a geometric approach, called n-CD, to provide realtime accurate LBSs while preserving users' location privacy without involving any third-party. Specifically, we first divide a user's region of interest (ROI), which is a disk centered at the user's location, into n equal sectors. Then, we generate n concealing disks (CDs), one for each sector, one by one to collaboratively and fully cover each of the n sectors. We call the area covered by the n CDs the concealing space, which fully contains the user's ROI. After rotating the concealing space with respect to the user's location, we send the rotated centers of the n CDs along with their radii to the service provider, instead of the user's real location and his/her ROI. To investigate the performance of n-CD, we theoretically analyze its privacy level and concealing cost. Extensive simulations are finally conducted to evaluate the efficacy and efficiency of the proposed schemes.

In the U.S., energy theft causes six billion dollar losses to utility companies (UCs) every year. With the smart grid being proposed to modernize current power grids, energy theft may become an even more serious problem since the “smart meters” used in smart grids are vulnerable to more types of attacks compared to traditional mechanical meters. Therefore, it is important to develop efficient and reliable methods to identify illegal users who are committing energy theft. Although some schemes have been proposed for the UCs to detect energy theft in power grids, they all require the users to send their private information, e.g., load files or meter readings at certain times, to the UCs which invades users' privacy and raises serious concerns about privacy, safety, etc. As far as we know, we are the first to investigate the energy theft detection problem considering users' privacy issues. In this paper, we propose to solve in a distributed fashion a linear system of equations (LSE) for the users' “honesty coefficients”, which indicate the users are honest when equal to 1 and are fraudulent when larger than 1. In particular, we develop two distributed privacy-preserving energy theft detection algorithms based on LU decomposition, called LUD and LUPD, respectively, which can identify fraudulent users without invading any user's privacy. Compared to LUD, LUPD requires higher execution time but is stable even in large-size systems. Moreover, the LUD and LUPD algorithms are proposed in the case that users commit energy theft at a constant rate, i.e., with constant honesty coefficients. We also propose adaptive LUD/LUPD algorithms to account for the scenarios where the users have variable honesty coefficients. Extensive simulations are carried out and the results show that the proposed algorithms can efficiently and successfully identify the fraudulent users in the system.

In common models of cognitive radio networks (CRNs), priority in spectrum access of primary users (PUs) must be guaranteed, but the quality of service (QoS) for secondary users (SUs) is mostly ignored. Subject to meeting PUs' blocking probability objective, this paper proposes a channel reservation scheme to enhance QoS for SUs. The proposed scheme employs a centralized control manager (CCM) to coordinate the dynamic spectrum access among PUs and SUs by dividing the spectrum into two parts: the normal access channels (NACs) that may be taken back anytime by PUs, and the reserved secondary channels (RSCs) that are locked by occupied SUs until their call sessions are complete. A Markov chain model is developed to analyze the proposed spectrum access scheme and compare its performance to that of a CRN without reserved channels. Furthermore, the number of reserved channels is optimized based on SUs' utility functions. Numerical results show that by optimally selecting the number of reserved channels to adapt to various traffic loads, the proposed scheme can significantly improve SUs' QoS while meeting PUs' QoS objectives.

In cognitive radio networks (CRN), underlay spectrum sharing allows secondary users (SUs) to utilize the spectrum on which the primary users (PUs) in primary radio networks (PRN) are working at the same time, without introducing intolerant interferences. In this paper, we study the joint transmission power and time allocation in CRN with underlay spectrum sharing technology. To maximize the total capacity of CRN and maintain the fairness for SUs, the system is modeled as an optimization problem with the constraints of interference and transmission power and time. Furthermore, we prove that the problem of joint optimization resource allocation under the constraints condition can be implemented independently from the time-frequency domain. Based on this study, we propose the optimal joint transmission power and time allocation method. Extensive simulation results show that the proposed optimal method can significantly improve the system capacity and maintain the fairness compared to the existing methods.

In order to fully utilize spectrum, auction-based dynamic spectrum access has become a promising approach which allows unlicensed wireless users to lease unused bands from spectrum license holders. Traditionally, the property of spectrum goes to a unique winner with the highest bid after the auction, and the bidders with low bids would be probably not served. This also may result in spectrum resource wasting when the assigned band is larger than the original request. In fact, because the spectrum is divisible goods, it can be shared among a group of users. In this paper, we propose a novel virtual bidder group (VBG) mechanism in spectrum auction which allows multiple winners to obtain the spectrum item simultaneously. Furthermore, a heuristic phase-optimization algorithm is proposed to reduce the crucial exponential computing time issue of band allocation optimization in double-side bandwidth auction scenario with multiple auctioneers. Simulation results prove that the VBG scheme could significantly improve the realized system data rate and the bandwidth utility, which indicates higher revenue for the spectrum license holder; meanwhile, the proposed phase-optimization algorithm exhibits relative low complexity and could provide a near-optimal performance.