BACKGROUND: The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures. RESULTS: In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment. CONCLUSIONS: We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

Recently, the automatic diagnosis of Turner syndrome (TS) has been paid more attention. However, existing methods relied on handcrafted image features. Therefore, we propose a TS classification method using unsupervised feature learning. Specifically, first, the TS facial images are preprocessed including aligning faces, facial area recognition and processing of image intensities. Second, pre-trained convolution filters are obtained by K-means based on image patches from TS facial images, which are used in a convolutional neural network (CNN); then, multiple recursive neural networks are applied to process the feature maps from the CNN to generate image features. Finally, with the extracted features, support vector machine is trained to classify TS facial images. The results demonstrate the proposed method is more effective for the classification of TS facial images, which achieves the highest accuracy of 84.95%.

Diagnosing infants who are small for gestational age (SGA) at early stages could help physicians to introduce interventions for SGA infants earlier. Machine learning (ML) is envisioned as a tool to identify SGA infants. However, ML has not been widely studied in this field. To develop effective SGA prediction models, we conducted four groups of experiments that considered basic ML methods, imbalanced data, feature selection and the time characteristics of variables, respectively. Infants with SGA data collected from 2010 to 2013 with gestational weeks between 24 and 42 were detected. Support vector machine (SVM), random forest (RF), logistic regression (LR) and Sparse LR models were trained on 10-fold cross validation. Precision and the area under the curve (AUC) of the receiver operator characteristic curve were evaluated. For each group, the performance of SVM and Sparse LR was similarly well. LR without any sparsity penalties performed worst, possibly caused by the overfitting problem. With the combination of handling imbalanced data and feature selection, the RF ensemble classifier performed best, which even obtained the highest AUC value (0.8547) with the help of expert knowledge. In other cases, RF performed worse than Sparse LR and SVM, possibly because of fully grown trees.

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

Dynamic spectrum access (DSA) is the key to solving worldwide spectrum shortage. The open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate users. Secondary-user authentication is thus critical to ensure the proper operations of DSA systems. This paper presents SpecGuard, the first crowdsourced spectrum misuse detection framework for DSA systems. In SpecGuard, a transmitter is required to embed a spectrum permit into its physical-layer signals, which can be decoded and verified by ubiquitous mobile users. We propose three novel schemes for embedding and detecting a spectrum permit at the physical layer. The first scheme relies on a higher transmission power to embed the spectrum permit. To alleviate the assumptions on the additional transmission power, the second scheme is proposed with a limited negative impact on the normal data transmission. The third scheme takes a different approach by adopting a novel constellation design and exploiting the trust between the transmitter and the receiver. Crowdsourced spectrum misuse detection eliminates the need for the deployment of dedicated sensors and thus greatly reduces the deployment and maintenance cost. Detailed theoretical analyses, MATLAB simulations, and USRP experiments confirm that our schemes can achieve correct, low-intrusive, and fast spectrum misuse detection.

Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9%, demonstrating the feasibility of POWERFUL.

Face authentication emerges as a powerful method for preventing unauthorized access to mobile devices. It is, however, vulnerable to photo-based forgery attacks (PFA) and videobased forgery attacks (VFA), in which the adversary exploits a photo or video containing the user's frontal face. Effective defenses against PFA and VFA often rely on liveness detection, which seeks to find a live indicator that the submitted face photo or video of the legitimate user is indeed captured in real time. In this paper, we propose FaceHeart, a novel and practical face authentication system for mobile devices. FaceHeart simultaneously takes a face video with the front camera and a fingertip video with the rear camera on COTS mobile devices. It then achieves liveness detection by comparing the two photoplethysmograms independently extracted from the face and fingertip videos, which should be highly consistent if the two videos are for the same live person and taken at the same time. As photoplethysmograms are closely tied to human cardiac activity and almost impossible to forge or control, FaceHeart is strongly resilient to PFA and VFA. Extensive user experiments on Samsung Galaxy S5 have confirmed the high efficacy and efficiency of FaceHeart.

With the popularity of social networks, Twitter has become one of the dominant providers of massive quantities of information.Exploring the distributions and correlations from Twitter data helps accurate personalized recommendations.Online Analytical Processing, or OLAP, provides an intuitive form that is suitable for exploring Twitter data.Unfortunately, the traditional OLAP approaches can only deal with structured data, not unstructured textual data like tweets.The key to applying OLAP to Twitter data is to mine and build a dimension hierarchy of tweeter interests.However, the current methods can extract tweeter interests from Twitter data on a single level, but fail to obtain a hierarchy of tweeter interests with different granularities.To address this problem, we propose a LDA-based model, called MS-LDA, which combines tweeters' social relationships and tweets to extract and build the tweeters' interest dimension hierarchy.Such a dimension hierarchy can be further employed to apply OLAP techniques to Twitter data.In addition, we employ Word2vec to obtain the linguistic similarity of words in tweets, to improve its effectiveness.The extensive experiments demonstrate that our method can effectively extract the dimension hierarchy of tweeters' interests for multidimensional analysis.

Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

Mobile device losses and thefts are skyrocketing. The sensitive data hosted on a lost/stolen device are fully exposed to the adversary. Although password-based authentication mechanisms are available on mobile devices, many users reportedly do not use them, and a device may be lost/stolen while in the unlocked mode. This paper presents the design and evaluation of iLock, a secure and usable defense against data theft on a lost/stolen mobile device. iLock automatically, quickly, and accurately recognizes the user's physical separation from his/her device by detecting and analyzing the changes in wireless signals. Once significant physical separation is detected, the device is immediately locked to prevent data theft. iLock relies on acoustic signals and requires at least one speaker and one microphone that are available on most COTS (commodity-off-the-shelf) mobile devices. Extensive experiments on Samsung Galaxy S5 show that iLock can lock the device with negligible false positives and negatives.

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

Device-to-device (D2D) communications are emerging due to the explosive growth of smartphones and tablets. Given the possible presence of attackers, a fundamental challenge in secure D2D communications is to develop sound mobile authentication techniques whereby mobile users can select the most trustworthy D2D communication partners from possibly many candidates. This paper tackles this open challenge and proposes spatiotemporal matching as a promising enabler for secure D2D communications. Spatiotemporal matching is built upon the location-aware capability of D2D devices. In particular, a mobile user could very easily maintain his spatiotemporal profile recording his continuous whereabouts in time, and the level of his spatiotemporal profile matching that of the other user can be translated into the level of trust they two can have in each other. Since spatiotemporal profiles contain very sensitive personal information, privacy-preserving spatiotemporal matching is needed to ensure that as little information as possible about the spatiotemporal profile of either matching participant is disclosed beyond the matching result. Toward this end, we propose two novel privacy-preserving spatiotemporal matching protocols, which are thoroughly analyzed and evaluated through detailed simulation studies driven by experimental data.

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

The plummeting cost of Bluetooth tags and the ubiquity of mobile devices are revolutionizing the traditional lost-and-found service. This paper presents SecureFind, a secure and privacy-preserving object-finding system via mobile crowdsourcing. In SecureFind, a unique Bluetooth tag is attached to every valuable object, and the owner of a lost object submits an object-finding request to many mobile users via the SecureFind service provider. Each mobile user involved searches his vicinity for the lost object on behalf of the object owner who can infer the location of his lost object based on the responses from mobile users. SecureFind is designed to ensure strong object security such that only the object owner can discover the location of his lost object as well as offering location privacy to mobile users involved. The high efficacy and efficiency of SecureFind are confirmed by extensive simulations.

Dynamic spectrum access (DSA) is the key to solving worldwide wireless spectrum shortage. In a DSA system, unlicensed secondary users can opportunistically use a spectrum band when it is not used by the licensed primary user. The open nature of the wireless medium means that any secondary user can freely use any given spectrum band. Secondary-user authentication is thus essential to ensure the proper operations of DSA systems. We propose SafeDSA, a novel PHY-based scheme for authenticating secondary users in DSA systems. In SafeDSA, the secondary user embeds his spectrum-use authorization into the cyclic prefix of each physical-layer symbol, which can be detected and authenticated by a verifier. In contrast to previous work, SafeDSA achieves robust and efficient authentication of secondary users with negligible impact on normal data transmissions. We validate the efficacy and efficiency of SafeDSA through detailed MATLAB simulations and USRP experiments. Our results show that SafeDSA can detect fake secondary users with a maximum false-positive rate of 0.091 and a negligible false-negative rate based on USRP experiments.

Multi-touch mobile devices have penetrated into everyday life to support personal and business communications. Secure and usable authentication techniques are indispensable for preventing illegitimate access to mobile devices. This paper presents RhyAuth, a novel two-factor rhythm-based authentication scheme for multi-touch mobile devices. RhyAuth requires a user to perform a sequence of rhythmic taps/slides on a device screen to unlock the device. The user is authenticated and admitted only when the features extracted from her rhythmic taps/slides match those stored on the device. RhyAuth is a two-factor authentication scheme that depends on a user-chosen rhythm and also the behavioral metrics for inputting the rhythm. Through a 32-user experiment on Android devices, we show that RhyAuth is highly secure against various attacks and also very usable for both sighted and visually impaired people.

Dynamic spectrum access is the key to solving worldwide spectrum shortage. The open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate users. This paper presents SpecGuard, the first crowdsourced spectrum misuse detection framework for DSA systems. In SpecGuard, a transmitter is required to embed a spectrum permit into its physical-layer signals, which can be decoded and verified by ubiquitous mobile users. We propose three novel schemes for embedding and detecting a spectrum permit at the physical layer. Detailed theoretical analyses, MATLAB simulations, and USRP experiments confirm that our schemes can achieve correct, low-intrusive, and fast spectrum misuse detection.

This paper considers a novel distributed system for collaborative location-based information generation and sharing which become increasingly popular due to the explosive growth of Internet-capable and location-aware mobile devices. The system consists of a data collector, data contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial top-k queries which ask for the POIs in a certain region and with the highest k ratings for an interested POI attribute. In practice, LBSPs are untrusted and may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. This paper presents three novel schemes for users to detect fake spatial snapshot and moving top-k query results as an effort to foster the practical deployment and use of the proposed system. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.

Secure neighbor discovery is fundamental to mobile ad hoc networks (MANETs) deployed in hostile environments and refers to the process in which two neighboring nodes exchange messages to discover and authenticate each other. It is vulnerable to the jamming attack in which the adversary intentionally transmits radio signals to prevent neighboring nodes from exchanging messages. Anti-jamming communications often rely on spread-spectrum techniques, which depend on a spreading code common to the communicating parties but unknown to the jammer. The spread code, however, is impossible to establish before the communicating parties successfully discover each other. While several elegant approaches have been recently proposed to break this circular dependence, the unique features of neighbor discovery in MANETs make them not directly applicable. In this paper, we propose JR-SND, a jamming-resilient secure neighbor discovery scheme for MANETs based on direct-sequence spread spectrum and random spread-code predistribution. JR-SND enables neighboring nodes to securely discover each other with overwhelming probability despite the presence of omnipresent jammers. Detailed theoretical and simulation results confirm the efficacy and efficiency of JR-SND.

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

This paper presents TIGHT, a geographic routing protocol for cognitive radio mobile ad hoc networks. TIGHT offers three routing modes and allows secondary users to fully explore the transmission opportunities over a primary channel without affecting primary users (PUs). The greedy mode routes a packet via greedy geographic forwarding until a PU region is encountered and then further routes the packet around the PU region to where greedy forwarding can resume. It works best when the PUs are only occasionally active. In contrast, the optimal and suboptimal modes route a packet along optimal and suboptimal trajectories to the destination, respectively. They work best when the PUs are active most of the time. The suboptimal mode is computationally more efficient than the optimal mode at the cost of using suboptimal trajectories in rare cases. The efficacy of TIGHT is confirmed by extensive simulations.

The explosive growth of mobile-connected and location-aware devices makes it possible to have a new way of establishing trust relationships, which we coin as spatiotemporal matching. In particular, a mobile user could very easily maintain his spatiotemporal profile recording his continuous whereabouts in time, and the level of his spatiotemporal profile matching that of the other user can be translated into the level of trust they two can have in each other. Since spatiotemporal profiles contain very sensitive personal information, privacy-preserving spatiotemporal matching is needed to ensure that as little information as possible about the spatiotemporal profile of either matching participant is disclosed beyond the matching result. We propose a cryptographic solution based on Private Set Intersection Cardinality and a more efficient non-cryptographic solution involving a novel use of the Bloom filter. We thoroughly analyze both solutions and compare their efficacy and efficiency via detailed simulation studies.

In this paper, we propose a novel method to generate the cyclostationary signature for Orthogonal Frequency Division Multiplexing (OFDM) signals with a low overhead of signature (i.e. number of signature subcarriers) and a good performance of the signature detection under frequency-selective fading channel, for dynamic spectrum access (DSA) networks. Moreover, we also present how to employ the proposed method to detect the embedded signature. Simulations are conducted to make a comparison of the detection performance between the proposed method and the Sutton method. Relying on single hopping subcarrier to generate the signature, the overhead of the proposed method is only one fourth that of the Sutton method in our example. Nevertheless, the simulation results showed that the detection performance of the proposed method is no worse than that of the method proposed by Sutton.