arXiv subjects

Xiang-Yang Li

Publications and source records attributed to Xiang-Yang Li.

90 records · Page 5Linked to original sources

arXiv2013

SilentSense: Silent User Identification via Dynamics of Touch and Movement Behavioral Biometrics

With the increased popularity of smartphones, various security threats and privacy leakages targeting them are discovered and investigated. In this work, we present \ourprotocoltight, a framework to authenticate users silently and transparently by exploiting dynamics mined from the user touch behavior biometrics and the micro-movement of the device caused by user's screen-touch actions. We build a "touch-based biometrics" model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, the unique operating dynamics of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movement-based biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99%.

cs.CR↗

arXiv2013

SmartLoc: Sensing Landmarks Silently for Smartphone Based Metropolitan Localization

We present \emph{SmartLoc}, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, \emph{SmartLoc} exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of \emph{SmartLoc} in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.

cs.NI↗

arXiv2013

Real-time Data Collection Scheduling in Multi-hop Wireless Sensor Networks

We study real time periodic query scheduling for data collection in multihop Wireless Sensor Networks (WSNs). Given a set of heterogenous data collection queries in WSNs, each query requires the data from the source sensor nodes to be collected to the control center within a certain end-to-end delay. We first propose almost-tight necessary conditions for a set of different queries to be schedulable by a WSN. We then develop a family of efficient and effective data collection algorithms that can meet the real-time requirement under resource constraints by addressing three tightly coupled tasks: (1) routing tree construction for data collection, (2) link activity scheduling, and (3) packet-level scheduling. Our theoretical analysis for the schedulability of these algorithms show that they can achieve a constant fraction of the maximum schedulable load. For the case of overloaded networks where not all queries can be possibly satisfied, we propose an efficient approximation algorithm to select queries to maximize the total weight of selected schedulable queries. The simulations corroborate our theoretical analysis.

cs.DC↗

arXiv2013

PPS: Privacy-Preserving Strategyproof Social-Efficient Spectrum Auction Mechanisms

Many spectrum auction mechanisms have been proposed for spectrum allocation problem, and unfortunately, few of them protect the bid privacy of bidders and achieve good social efficiency. In this paper, we propose PPS, a Privacy Preserving Strategyproof spectrum auction framework. Then, we design two schemes based on PPS separately for 1) the Single-Unit Auction model (SUA), where only single channel to be sold in the spectrum market; and 2) the Multi-Unit Auction model (MUA), where the primary user subleases multi-unit channels to the secondary users and each of the secondary users wants to access multi-unit channels either. Since the social efficiency maximization problem is NP-hard in both auction models, we present allocation mechanisms with approximation factors of $(1+ε)$ and 32 separately for SUA and MUA, and further judiciously design strategyproof auction mechanisms with privacy preserving based on them. Our extensive evaluations show that our mechanisms achieve good social efficiency and with low computation and communication overhead.

cs.NI↗

arXiv2013

Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-based Speed Estimation

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location based service, and Wi-Fi fingerprint-based approaches are the most popular and inexpensive solutions. Among them, one of the main trends is to incorporate the built-in sensors of smartphone and to exploit crowdsourcing potentials. However the noisy built-in sensors and multi-tasking limitation of underline OS often hinder the effectiveness of these schemes. In this work, we propose a passive crowdsourcing CSI-based indoor localization scheme, C2 IL. Our scheme C2 IL only requires the locating-device (e.g., a phone) to have a 802.11n wireless connection, and it does not rely on inertial sensors only existing in some smartphones. C2 IL is built upon our innovative method to accurately estimate the moving distance purely based on 802.11n Channel State Information (CSI). Our extensive evaluations show that the moving distance estimation error of our scheme is within 3% of the actual moving distance regardless of varying speeds and environment. Relying on the accurate moving distance estimation as constraints, we are able to construct a more accurate mapping between RSS fingerprints and location. To address the challenges of collecting fingerprints, a crowdsourcing- based scheme is designed to gradually establish the mapping and populate the fingerprints. In C2 IL, we design a trajectory clustering-based localization algorithm to provide precise real-time indoor localization and tracking. We developed and deployed a practical working system of C2 IL in a large office environment. Extensive evaluation results indicate that our scheme C2 IL provides accurate localization with error 2m at 80% at very complex indoor environment with minimal overhead.

cs.NI↗

arXiv2013

TEXIVE: Detecting Drivers Using Personal Smart Phones by Leveraging Inertial Sensors

In this work, we address a fundamental and critical task of detecting the behavior of driving and texting using smartphones carried by users. We propose, design, and implement TEXIVE that leverages various sensors integrated in the smartphone and realizes our goal of distinguishing drivers and passengers and detecting texting using rich user micro-movements and irregularities that can be detected by sensors in the phone before and during driving and texting. Without relying on external infrastructure, TEXIVE has an advantage of being readily implemented and adopted, while at the same time raising a number of challenges that need to be carefully addressed for achieving a successful detection with good sensitivity, specificity, accuracy, and precision. Our system distinguishes the driver and passengers by detecting whether a user is entering a vehicle or not, inferring which side of the vehicle s/he is entering, reasoning whether the user is siting in front or rear seats, and discovering if a user is texting by fusing multiple evidences collected from accelerometer, magnetometer, and gyroscope sensors. To validate our approach, we conduct extensive experiments with several users on various vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

cs.NI↗

arXiv2013

OMG: How Much Should I Pay Bob in Truthful Online Mobile Crowdsourced Sensing?

Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of the pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more real scenario where users arrive one by one online in a random order. We model the problem as an online auction in which the users submit their private types to the crowdsourcer over time, and the crowdsourcer aims to select a subset of users before a specified deadline for maximizing the total value of the services provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.

cs.GT↗

arXiv2013

Accurate Indoor Localization Using Acoustic Direction Finding via Smart Phones

We propose and implement a novel indoor localization scheme, Swadloon, built upon an accurate acoustic direction finding. Swadloon leverages sensors of the smartphone without the requirement of any specialized devices. The scheme Swadloon does not rely on any fingerprints and is very easy to use: a user only needs to shake the phone for a short duration before walking and localization. Our Swadloon design exploits a key observation: the relative shift and velocity of the phone-shaking movement corresponds to the subtle phase and frequency shift of the Doppler effects experienced in the received acoustic signal by the phone. A novel method is designed to derive the direction from the phone to the acoustic source by combining the velocity calculated from the subtle Doppler shift with the one from the inertial sensors of the phone. Then a real-time precise localization and tracking is enabled by using a few anchor speakers with known locations. Major challenges in implementing Swadloon are to measure the frequency shift precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, then design and deploy Swadloon in several floors of an indoor building each with area about 2000m^2. Our extensive experiments show that the mean error of direction finding is around 2.1 degree when the acoustic source is within the range of 32m. For indoor localization, the 90-percentile errors are under 0.92m, while the maximum error is 1.73m and the mean is about 0.5m. For real-time tracking, the errors are within 0.4m for walks of 51m.

cs.NI↗

arXiv2013

Near-Optimal Truthful Auction Mechanisms in Secondary Spectrum Markets

In this work, we study spectrum auction problem where each request from secondary users has spatial, temporal, and spectral features. With the requests of secondary users and the reserve price of the primary user, our goal is to design truthful mechanisms that will either maximize the social efficiency or maximize the revenue of the primary user. As the optimal conflict-free spectrum allocation problem is NP-hard, in this work, we design near optimal spectrum allocation mechanisms separately based on the following techniques: derandomized allocation from integer programming formulation, its linear programming (LP) relaxation, and the dual of the LP. We theoretically prove that 1) our near optimal allocation methods are bid monotone, which implys truthful auction mechanisms; and 2) our near optimal allocation methods can achieve a social efficiency or a revenue that is at least $1-\frac{1}{e}$ times of the optimal respectively. At last, we conduct extensive simulations to study the performances (social efficiency, revenue) of the proposed methods, and the simulation results corroborate our theoretical analysis.

cs.NI↗

arXiv2013

Rejecting the Attack: Source Authentication for Wi-Fi Management Frames using CSI Information

Comparing to well protected data frames, Wi-Fi management frames (MFs) are extremely vulnerable to various attacks. Since MFs are transmitted without encryption, attackers can forge them easily. Such attacks can be detected in cooperative environment such as Wireless Intrusion Detection System (WIDS). However, in non-cooperative environment it is difficult for a single station to identify these spoofing attacks using Received Signal Strength (RSS)-based detection, due to the strong correlation of RSS to both the transmission power (Txpower) and the location of the sender. By exploiting some unique characteristics (i.e., rapid spatial decorrelation, independence of Txpower, and much richer dimensions) of the Channel State Information (CSI), a standard feature in 802.11n Specification, we design a prototype, called CSITE, to authenticate the Wi-Fi management frames by a single station without external support. Our design CSITE, built upon off-the-shelf hardware, achieves precise spoofing detection without collaboration and in-advance finger-print. Several novel techniques are designed to address the challenges caused by user mobility and channel dynamics. To verify the performances of our solution, we implement a prototype of our design and conduct extensive evaluations in various scenarios. Our test results show that our design significantly outperforms the RSS-based method in terms of accuracy, robustness, and efficiency: we observe about 8 times improvement by CSITE over RSS-based method on the falsely accepted attacking frames.

cs.NI↗

arXiv2013

Link Scheduling for Throughput Maximization in Multihop Wireless Networks Under Physical Interference

We consider the problem of link scheduling for throughput maximization in multihop wireless networks. Majority of previous methods are restricted to graph-based interference models. In this paper we study the link scheduling problem using a more realistic physical interference model. Through some key observations about this model, we develop efficient link scheduling algorithms by exploiting the intrinsic connections between the physical interference model and the graph-based interference model. For one variant of the problem where each node can dynamically adjust its transmission power, we design a scheduling method with O(g(E)) approximation to the optimal throughput capacity where g(E) denotes length diversity. For the other variant where each node has a fixed but possible different transmission powers for different nodes, we design a method with O(g(E))-approximation ratio when the transmission powers of all nodes are within a constant factor of each other, and in general with an approximation ratio of O(g(E)logρ) where logρis power diversity. We further prove that our algorithm for fixed transmission power case retains O(g(E)) approximation for any length-monotone, sub-linear fixed power setting. Furthermore, all these approximation factors are independent of network size.

cs.NI↗

arXiv2013

AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption

Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. However, those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute- Based Encryption (ABE) have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address the user and data privacy problem in a cloud. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access, finegrained privilege control, and more importantly, tolerance to up to (N -2) authority compromise. Our security and performance analysis show that AnonyControl is both secure and efficient for cloud computing environment.

cs.CR↗

arXiv2013

Data Aggregation without Secure Channel: How to Evaluate a Multivariate Polynomial Securely

Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either assume to have a secure channel or suffer from high complexity. Here we consider how an external aggregator or multiple parties learn some algebraic statistics (e.g., summation, product) over participants' data while any individual's input data is kept secret to others (the aggregator and other participants). We assume channels in our construction are insecure. That is, all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We successfully guarantee data confidentiality under this weak assumption while limiting both the communication and computation complexity to at most linear.

cs.CR↗

arXiv2013

Search Me If You Can: Privacy-preserving Location Query Service

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Many LBS providers use users' location information to offer them convenience and useful functions. However, the LBS could greatly breach personal privacy because location itself contains much information. Hence, preserving location privacy while achieving utility from it is still an challenging question now. This paper tackles this non-trivial challenge by designing a suite of novel fine-grained Privacy-preserving Location Query Protocol (PLQP). Our protocol allows different levels of location query on encrypted location information for different users, and it is efficient enough to be applied in mobile platforms.

cs.CR↗

arXiv2012

MLLS: Minimum Length Link Scheduling Under Physical Interference Model

We study a fundamental problem called Minimum Length Link Scheduling (MLLS) which is crucial to the efficient operations of wireless networks. Given a set of communication links of arbitrary length spread and assume each link has one unit of traffic demand in wireless networks, the problem MLLS seeks a schedule for all links (to satisfy all demands) of minimum number of time-slots such that the links assigned to the same time-slot do not conflict with each other under the physical interference model. In this paper, we will explore this problem under three important transmission power control settings: linear power control, uniform power control and arbitrary power control. We design a suite of new and novel scheduling algorithms and conduct explicit complexity analysis to demonstrate their efficiency. Our algorithms can account for the presence of background noises in wireless networks. We also investigate the fractional case of the problem MLLS where each link has a fractional demand. We propose an efficient greedy algorithm of the approximation ratio at most $(K+1)^{2}ω$.

cs.NI↗

arXiv2012

Efficient Construction of Dominating Set in Wireless Networks

Considering a communication topology of a wireless network modeled by a graph where an edge exists between two nodes if they are within each other's communication range. A subset $U$ of nodes is a dominating set if each node is either in $U$ or adjacent to some node in $U$. Assume each node has a disparate communication range and is associated with a positive weight, we present a randomized algorithm to find a min-weight dominating set. Considering any orientation of the graph where an arc $\overrightarrow{uv}$ exists if the node $v$ lies in $u$'s communication range. A subset $U$ of nodes is a strongly dominating set if every node except $U$ has both in-neighbor(s) and out-neighbor(s) in $U$. We present a polynomial-time algorithm to find a strongly dominating set of size at most $(2+ε)$ times of the optimum. We also investigate another related problem called $K$-Coverage. Given are a set ${\cal D}$ of disks with positive weight and a set ${\cal P}$ of nodes. Assume all input nodes lie below a horizontal line $l$ and all input disks lie above this line $l$ in the plane. The objective is to find a min-weight subset ${\cal D}'\subseteq {\cal D}$ of disks such that each node is covered at least $K$ disks in ${\cal D}'$. We propose a novel two-approximation algorithm for this problem.

cs.NI↗

arXiv2012

Message in a Sealed Bottle: Privacy Preserving Friending in Social Networks

Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with matched profile within a certain distance. A challenging task in these applications is to protect the privacy the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search a person with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms are privacy-preserving: no participants' profile and the submitted preference-profile are exposed. Our mechanisms establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is secure, privacy-preserving, verifiable, and efficient both in communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient then existing solutions.

cs.SI↗

arXiv2012

Network Agile Preference-Based Prefetching for Mobile Devices

For mobile devices, communication via cellular networks consumes more energy, and has a lower data rate than WiFi networks, and suffers an expensive limited data plan. However the WiFi network coverage range and density are smaller than those of the cellular networks. In this work, we present a behavior-aware and preference-based approach to prefetch news webpages that a user will be interested in and access, by exploiting the WiFi network connections to reduce the energy and monetary cost. In our solution, we first design an efficient preference learning algorithm based on keywords and URLs visited, which will keep track of the user's changing interests. By predicting the appearance and durations of the WiFi network connections, our prefetch approach then optimizes when to prefetch what webpages to maximize the user experience while lowing the prefetch cost. Our prefetch approach exploits the idle period of WiFi connections to reduce the tail-energy consumption. We implement our approach in iPhone. Our extensive evaluations show that our system achieves about 60% hit ratio, saves about 50% cellular data usage, and reduces the energy cost by 9%.

cs.NI↗