Federated stochastic bilevel optimization has been actively studied in recent years due to its widespread applications in machine learning. However, most existing federated stochastic bilevel optimization algorithms require the computation of second-order Hessian and Jacobian matrices, which leads to longer running times in practice. To address these challenges, we propose a novel federated stochastic variance-reduced bilevel gradient descent algorithm that relies solely on first-order oracles. Specifically, our approach does not require the computation of second-order Hessian and Jacobian matrices, significantly reducing running time. Furthermore, we introduce a novel learning rate mechanism, i.e., a constant single-time-scale learning rate, to coordinate the update of different variables. We also present a new strategy to establish the convergence rate of our algorithm. Finally, the extensive experimental results confirm the efficacy of our proposed algorithm.

Extracting skill utterances is a crucial step in testing and evaluating smart speaker skills. Previous works have proposed various techniques for extracting utterances from the skill web page to test and evaluate smart speaker skills. In this article, we evaluate the effectiveness and correctness of different utterance-extracting techniques proposed in previous works. Our evaluation reveals that some techniques can capture incorrect utterances that the skill will not accept as spoken utterances. We also find that all of the proposed techniques would never capture the total utterances supported by skills, and combining these techniques yields the best results in terms of text parsing. To address these limitations, we propose a new technique that combines the strengths of previous techniques and leverages human to interact with a small set of skills to expand the coverage for testing other skills. We evaluate the effectiveness of our technique and demonstrate that it can capture a higher number of utterances supported by skills. We delved into the impact assessment of utterance extraction, aiming to enhance the thoroughness and effectiveness of skill testing. We conducted an impact study on 11 skills to assess the importance of utterance extraction in the context of skills testing. Our findings demonstrate that our technique captures a higher number of utterances compared to previous techniques. Through our evaluation, we provide insights into the significance of discovering more utterances in the testing context, and demonstrate the effectiveness of our proposed technique in capturing more utterances for skill testing and evaluation.

The search for smart contract source codes has drawn research attention to fulfill developers’ and researchers’ needs. Yet, the existing studies are not mature enough to address smart contracts’ technical properties and functionalities. This paper proposes a system to improve the naive search for smart contract codes; for example, Etherscan has one keyword search feature without regard to the contract structure. We consider clustering smart contracts based on developers’ preferences, which increases the probability that the resulting source codes match developers’ needs. Our experimental results show a significant improvement in the complexity of the retrieved source codes of smart contracts compared with the baseline scenario using blockchain search engines (e.g., Etherscan). Our solution reduces the number of retrieved smart contract codes the developer has to check if the codes match her/his needs by 94%, 88%, 82%, or 98%, depending on the user’s search preferences.

In this paper, we present ARCHIE++, a testing framework for conducting AR system testing and collecting user feedback in the wild. Our system addresses challenges in AR testing practices by aggregating usability feedback data (collected in situ) with system performance data from that same time period. These data packets can then be leveraged to identify edge cases encountered by testers during unconstrained usage scenarios. We begin by presenting a set of current trends in performing human testing of AR systems, identified by reviewing a selection of recent work from leading conferences in mixed reality, human factors, and mobile and pervasive systems. From the trends, we identify a set of challenges to be faced when attempting to adopt these practices to testing in the wild. These challenges are used to inform the design of our framework, which provides a cloud-enabled and device-agnostic way for AR systems developers to improve their knowledge of environmental conditions and to support scalability and reproducibility when testing in the wild. We then present a series of case studies demonstrating how ARCHIE++ can be used to support a range of AR testing scenarios, and demonstrate the limited overhead of the framework through a series of evaluations. We close with additional discussion on the design and utility of ARCHIE++ under various edge conditions.

In this work, we focus on the label layout problem: specifying the positions of overlaid virtual annotations in Virtual/Augmented Reality scenarios. Designing a layout of labels that does not violate domain-specific design requirements, while at the same time satisfying aesthetic and functional principles of good design, can be a daunting task even for skilled visual designers. Presenting the annotations in 3D object space instead of projection space, allows for the preservation of spatial and depth cues. This results in stable layouts in dynamic environments, since the annotations are anchored in 3D space. In this paper we make two major contributions. First, we propose a technique for managing the layout and rendering of annotations in Virtual/Augmented Reality scenarios by manipulating the annotations directly in 3D space. For this, we make use of Artificial Potential Fields and use 3D geometric constraints to adapt them in 3D space. Second, we introduce PartLabeling: an open source platform in the form of a web application that acts as a much-needed generic framework allowing to easily add labeling algorithms and 3D models. This serves as a catalyst for researchers in this field to make their algorithms and implementations publicly available, as well as ensure research reproducibility. The PartLabeling framework relies on a dataset that we generate as a subset of the original PartNet dataset [17] consisting of models suitable for the label management task. The dataset consists of 1,000 3D models with part annotations.

The ability of Voice User Interface (VUI) to understand how users will express their commands naturally and intuitively is an essential component of user experience, especially when the user is interacting with the VUI for the first time. Designing an automated method for testing the usability of VUI is a challenge for two reasons. First, there are many different ways for a user to express the same intention, e.g. “play some music”, ””put some music on”, etc., that is difficult to determine in advance. Second, many VUI apps today typically rely on the platform service provider (e.g. Amazon, Google, etc.) to perform many of the speech recognition and natural language processing tasks, and these services are provided as a blackbox. Consequently, it is difficult for the app developer to obtain information about errors and user feedback. In this paper, we propose a framework, VORI, to systematically evaluate the interactability of VUI, as well as a new metric for quantifying the interactability of a VUI. We use VORI to analyze 127 applications on Alexa by sending over 82,931 commands. Our analysis results highlight that 41.7% of apps only accept strict input that has to exactly match the developer’s predefined sample commands with an interactability score of 20% or less. This suggests developers should consider a better interactability strategy in the design of VUIs, and more research is needed to further explore the design space to improve the interactability.

Mobile augmented reality systems are becoming increasingly common and powerful, with applications in such domains as healthcare, manufacturing, education, and more. This rise in popularity is thanks in part to the functionalities offered by commercially available vision libraries such as ARCore, Vuforia, and Google’s ML Kit; however, these libraries also give rise to the possibility of a hidden operations threat , that is, the ability of a malicious or incompetent application developer to conduct additional vision operations behind the scenes of an otherwise honest AR application without alerting the end-user. In this article, we present the privacy risks associated with the hidden operations threat and propose a framework for application development and runtime permissions targeted specifically at preventing the execution of hidden operations. We follow this with a set of experimental results, exploring the feasibility and utility of our system in differentiating between user-expectation-compliant and non-compliant AR applications during runtime testing, for which preliminary results demonstrate accuracy of up to 71%. We conclude with a discussion of open problems in the areas of software testing and privacy standards in mobile AR systems.

Deep networks have been used for semantic segmentation tasks on scenes of outdoor environments with increasing popularity. However, the majority of existing work centers on daytime scenes with favorable illumination and weather conditions, and relies on supervision with pixel-level annotations. This paper seeks to address the problem of semantic segmentation for rainy, night-time scenes without using pixel-level annotations. We introduce a near scene semantic approach that uses images of daytime scenes as a bridge for transferring knowledge from pre-trained segmentation models to rainy night images. Specifically, we first present near scene oriented Representation Adaptation (RA) to reduce the domain shift on the representation level. Next, we adapt the segmentation model from the daytime scenario, under varying weather conditions, to the rainy night scenario by using near scene oriented Segmentation Space Adaptation (SSA). Consequently, this further reduces the impact of the domain shift on the segmentation space level. For evaluation, we created a new dataset containing 7000 distinct daytime-night-time image pairs of near scenes obtained by a webcam, and 5266 daytime-rainy night image pairs collected by a car-mounted camera. In addition, we carefully annotated 226 rainy night images with classes defined in Cityscapes. The experimental results clearly demonstrate the advantage of the proposed algorithm.

The rapid increase of the world’s urbanization process has been improving citizens’ quality of living. Combining new technologies of smart government, smart healthcare, smart transportation and other services under a framework of smart city minimizes urbanization challenges. However, these services demand a large data technology to support the infrastructure of smart cities. It is a major benefit to using blockchain technology as a framework to integrate multiple technologies of smart city such as Internet of Thing, big data platforms and smart transportation to enhance the automation, security and decentralization of smart city services. However, querying the blockchain to retrieve a transaction record is one of the major limitations of blockchain systems. The operation requires scanning blockchain ledger searching for results. In this paper, we utilized different smart contract designs to support indexing and querying the blockchain for ride sharing data. Our experiments evaluate the complexity of two smart contract designs, Catalog and Sparse smart contracts for indexing and retrieving data from the blockchain

In this paper, we present ARCHIE, a framework for testing augmented reality applications in the wild. ARCHIE collects user feedback and system state data in situ to help developers identify and debug issues important to testers. It also supports testing of multiple application versions (called "profiles") in a single evaluation session, prioritizing those versions which the tester finds more appealing. To evaluate ARCHIE, we implemented four distinct test case applications and used these applications to examine the performance overhead and context switching cost of incorporating our framework into a pre-existing code base. With these, we demonstrate that ARCHIE provides no significant overhead for AR applications, and introduces at most 2% processing overhead when switching among large groups of testable profiles.

Image alignment/registration/correspondence is a critical prerequisite for many vision-based tasks, and it has been widely studied in computer vision. However, aligning images from different domains, such as cross-weather/season road scenes, remains a challenging problem. Inspired by the success of classic intensity-constancy-based image alignment methods and the modern generative adversarial network (GAN) technology, we propose a cross-weather road scene alignment method called latent generative model with intensity constancy. From a novel perspective, the alignment problem is formulated as a constrained 2D flow optimization problem with latent encoding, which can be decoded into an intensity-constancy image on the latent image manifold. The manifold is parameterized by a pre-trained GAN, which is able to capture statistic characteristics from large datasets. Moreover, we employ the learned manifold to constrain the warped latent image identical to the target image, thereby producing a realistic warping effect. Experimental results on several cross-weather/season road scene datasets demonstrate that our approach can significantly outperform the state-of-the-art methods.

Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become a severe threat to the trustworthiness of machine learning. While AEs in the image domain have been well studied, audio AEs are less investigated. Recently, multiple techniques are proposed to generate audio AEs, which makes countermeasures against them urgent. Our experiments show that, given an audio AE, the transcription results by Automatic Speech Recognition (ASR) systems differ significantly (that is, poor transferability), as different ASR systems use different architectures, parameters, and training datasets. Based on this fact and inspired by Multiversion Programming, we propose a novel audio AE detection approach MVP-Ears, which utilizes the diverse off-the-shelf ASRs to determine whether an audio is an AE. We build the largest audio AE dataset to our knowledge, and the evaluation shows that the detection accuracy reaches 99.88%. While transferable audio AEs are difficult to generate at this moment, they may become a reality in future. We further adapt the idea above to proactively train the detection system for coping with transferable audio AEs. Thus, the proactive detection system is one giant step ahead of attackers working on transferable AEs.

Recent progresses in model-free single object tracking (SOT) algorithms have largely inspired applying SOT to multi-object tracking (MOT) to improve the robustness as well as relieving dependency on external detector. However, SOT algorithms are generally designed for distinguishing a target from its environment, and hence meet problems when a target is spatially mixed with similar objects as observed frequently in MOT. To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models. In particular, we construct each target model by fusing information for distinguishing target both from background and other instances (tracking targets). To conserve uniqueness of all target models, our instance-aware tracker considers response maps from all target models and assigns spatial locations exclusively to optimize the overall accuracy. Another contribution we make is a dynamic model refreshing strategy learned by a convolutional neural network. This strategy helps to eliminate initialization noise as well as to adapt to variation of target size and appearance. To show the effectiveness of the proposed approach, it is evaluated on the popular MOT15 and MOT16 challenge benchmarks. On both benchmarks, our approach achieves the best overall performances in comparison with published results.

The MapReduce framework and its open source implementation Hadoop have become the defacto platform for scalable analysis on large data sets in recent years. One of the primary concerns in Hadoop is how to minimize the completion length (i.e., makespan) of a set of MapReduce jobs. The current Hadoop only allows static slot configuration, i.e., fixed numbers of map slots and reduce slots throughout the lifetime of a cluster. However, we found that such a static configuration may lead to low system resource utilizations as well as long completion length. Motivated by this, we propose simple yet effective schemes which use slot ratio between map and reduce tasks as a tunable knob for reducing the makespan of a given set. By leveraging the workload information of recently completed jobs, our schemes dynamically allocates resources (or slots) to map and reduce tasks. We implemented the presented schemes in Hadoop V0.20.2 and evaluated them with representative MapReduce benchmarks at Amazon EC2. The experimental results demonstrate the effectiveness and robustness of our schemes under both simple workloads and more complex mixed workloads.

Securing a wireless channel between any two vehicles is a crucial component of vehicular networks security. This can be done by using a secret key to encrypt the messages. We propose a scheme to allow two cars to extract a shared secret from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same key. The key is information-theoretically secure, i.e., it is secure against an adversary with unlimited computing power. Although there are existing solutions of key extraction in the indoor or low-speed environments, the unique channel conditions make them inapplicable to vehicular environments. Our scheme effectively and efficiently handles the high noise and mismatch features of the measured samples so that it can be executed in the noisy vehicular environment. We also propose an online parameter learning mechanism to adapt to different channel conditions. Extensive real-world experiments are conducted to validate our solution.

Mobile peer-to-peer applications require devices to network themselves on-the-fly to communicate directly with each another. This paper presents mQual, a framework to help create such networks that meet different application requirements, and is able to adjust the network to ensure that these requirements are met in dynamic environments. Our prototype mQual extends the current WiFi-Direct in Android, and the experimental results suggests that mobile apps built using mQual outperform those built using WiFi-Direct.

This study investigated several smartphone-based applications designed for citizens to record encounters with law enforcement officers. The study aimed to analyze the different applications based on their initialization speed, ease of recording, upload speed and robustness. The study also sought to provide the developers of future applications with insight into what attributes are most important to this type of applications. We found that in general, applications of this kind do not always function consistently. The design features that proved the most beneficial were uploading in a broadcast style, providing ample information regarding the status of an upload, and saving a local copies of videos recorded in the app.

Elasticity is the ability to scale computing resources such as memory on-demand, and is one of the main advantages of utilizing cloud computing services. With the increasing popularity of cloud based storage, it is natural that more deduplication based storage systems will be migrated to the cloud. Existing deduplication systems however, do not adequately take advantage of elasticity. In this paper, we illustrate how to use elasticity to improve deduplication based systems, and propose EAD (elasticity aware deduplication), an indexing algorithm that uses the ability to dynamically increase memory resources to improve overall deduplication performance. Our experimental results indicate that EAD is able to detect more than 98\% of all duplicate data, however only consumes less than 5\% of expected memory space. Meanwhile, it claims four times of deduplication efficiency than the state-of-art sampling technique while costs less than half of the amount of memory.

In this paper, we present a novel scheme for auditing Service Level Agreement (SLA) in a semi-trusted or untrusted cloud. A SLA is a contract formed between a cloud service provider (CSP)and a user which specifies, in measurable terms, what resources a the CSP will provide the user. CSP's being profit based companies have incentive to cheat on the SLA. By providing a user with less resources than specified in the SLA the CSP can support more users on the same hardware and increase their profits. As the monitoring and verification of the SLA is typically performed on the cloud system itself it is straightforward for the CSP to lie on reports and hide their intentional breach of the SLA. To prevent such cheating we introduce a framework which makes use of a third party auditor (TPA). In this paper we are interested in CPU cheating only. To detect CPU cheating, we develop an algorithm which makes use of a commonly used CPU intensive calculation, transpose matrix multiplication, to randomly detect cheating by a CSP. Using real experiments we show that our algorithm can detect CPU cheating quite effectively even if the extent of the cheating is fairly small.

The growing popularity of Android-based smart-phones have led to the rise of Android based malware. In particular, profit-motivated malware is becoming increasingly popular in Android malware distribution. These malware typically profit by sending premium-rate SMS messages and/or make premium-rate phone calls from infected devices without user consent. In this paper, we investigate the telephony framework of the Android operating system and propose a novel process user-identification (UID) based online detection scheme. Our scheme can effectively detect premium-rate and background SMS messages as well as premium-rate phone calls initiated by malware. We implemented our detection system on a Samsung Google Nexus 4 running Android Jelly Bean and tested the effectiveness of detecting real malware from Android markets. The experimental results show that our scheme is efficient and effective in detecting background messages and premium-rate messages and phone calls. Our scheme can detect and block all the background and premium-rate SMS messages and phone calls initiated by popular malware.

The IEEE 802.16 standard (WiMAX) is an important next-generation networking technology which promises high-speed network access for both mobile and fixed users. In this paper we present a method to estimate link quality for devices connected to Temple University's WiMAX network as they traverse both the main campus and the city of Philadelphia via foot and motor vehicle. This is accomplished by first measuring receive signal strength indicator (RSSI), carrier to interference plus noise ratio (CINR), and bandwidth. After capturing these values, we then analyze the data to provide an estimation of the actual system rate. We then present an approach to predict future states of link quality both while stationary at Temple and when traversing Philadelphia via bus.

For this study the techniques of virtual machine migration are understood and the affects deduplication has on migration are evaluated. The benefits of using deduplication and compression on virtual machines show in the metric of space saved during migrating. Deduplication is computationally expensive so we evaluate how to group virtual machines with similar elements in order to improve migration. From this study, grouping virtual machines based on similar elements improves the overhead from deduplication and compression but estimates which virtual machines are best grouped together.

Wi-Fi tethering (i.e., sharing the Internet connection of a mobile phone via its Wi-Fi interface) is a useful functionality and is widely supported on commercial smartphones. Yet, existing Wi-Fi tethering schemes consume excessive power: they keep the Wi-Fi interface in a high power state regardless if there is ongoing traffic or not. In this paper, we propose DozyAP to improve the power efficiency of Wi-Fi tethering. Based on measurements in typical applications, we identify many opportunities that a tethering phone could sleep to save power. We design a simple yet reliable sleep protocol to coordinate the sleep schedule of the tethering phone with its clients without requiring tight time synchronization. Furthermore, we develop a two-stage, sleep interval adaptation algorithm to automatically adapt the sleep intervals to ongoing traffic patterns of various applications. DozyAP does not require any changes to the 802.11 protocol and is incrementally deployable through software updates. We have implemented DozyAP on commercial smartphones. Experimental results show that, while retaining comparable user experiences, our implementation can allow the Wi-Fi interface to sleep for up to 88% of the total time in several different applications and reduce the system power consumption by up to 33% under the restricted programmability of current Wi-Fi hardware.

Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In a cost-efficient cloud environment, a user can tolerate a certain degree of delay while retrieving information from the cloud to reduce costs. In this paper, we address two fundamental issues in such an environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme that was originally proposed by Ostrovsky. Their scheme allows a user to retrieve files of interest from an untrusted server without leaking any information. The main drawback is that it will cause a heavy querying overhead incurred on the cloud and thus goes against the original intention of cost efficiency. In this paper, we present three efficient information retrieval for ranked query (EIRQ) schemes to reduce querying overhead incurred on the cloud. In EIRQ, queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. A user can retrieve files on demand by choosing queries of different ranks. This feature is useful when there are a large number of matched files, but the user only needs a small subset of them. Under different parameter settings, extensive evaluations have been conducted on both analytical models and on a real cloud environment, in order to examine the effectiveness of our schemes.

In this paper, a user-based CPU verification scheme is proposed for cloud cheating detection. In this scheme, a predefined computational task is constructed for the cloud to execute in our cheating detection process. Then we compare the difference of the actual execution time (recorded by the user) and the theoretical execution time, as to determine whether the cloud is cheating or not. A time-lock puzzle is introduced to construct the predefined computational task, so that the predefined computational task is guaranteed to be executed by the cloud. Our cheating detection process has a higher probability of detecting cloud cheating if using a larger predefined computational task, which in turn costs more time. Further analysis shows that, if the total detection time is limited, it is better to detect cloud cheating using small-scale and short-length cheating detecting processes multiple times, as opposed to large-scale and long-length processes a few times. Finally, the feasibility and validity of the proposed scheme is shown in the evaluations.

Mobile social networks are vulnerable to Sybil attacks. By creating a large number of fake identities, malicious users can gain a disproportionately high benefit through a Byzantine fashion. Most social network-based Sybil defenses adopt the assumptions that the honest region is a fast-mixing network. However, more and more evidence shows that some real social networks are not fast-mixing, especially when only strong-trust relations are considered. Moreover, the accuracy of all existing solutions is related to the number of attack edges that the adversary can build. In this paper, for addressing these problems, we propose a local ranking system for estimating trust-level between users. Our scheme has three unique features. First, our system is based on both trust and distrust relations. Second, instead of storing the entire social graph, users carry limited information related to themselves. Last but not least, our system weakens the impacts of attack edges by removing several suspicious edges with high centrality. We validate the effectiveness of our solutions through comprehensive experiments.

Efficient web caching in mobile apps eliminates unnecessary network traffic, reduces web accessing latency, and improves smartphone battery life. However, recent research has indicated that current mobile apps suffer from poor implementations of web caching. In this work, we first conducted a comprehensive survey of over 1000 Android apps to identify how different types of mobile apps perform in web caching. Based on our analysis, we designed CacheKeeper, an OS web caching service transparent to mobile apps for smartphones. CacheKeeper can not only effectively reduce overhead caused by poor web caching of mobile apps, but also utilizes cross-app caching opportunities in smartphones. Furthermore, CacheKeeper is backward compatible, meaning that existing apps can take advantage of CacheKeeper without any modifications. We have implemented a prototype of CacheKeeper in Linux kernel. Evaluation on 10 top ranked Android apps shows that our CacheKeeper prototype can save 42% networks traffic with real user browsing behaviors and increase web accessing speed by 2x under real 3G settings. Experiments also show that our prototype incurs negligible overhead in most aspects on cache misses.

A crucial component of vehicular network security is to establish a secure wireless channel between any two vehicles. In this paper, we propose a scheme to allow two cars to extract a secret key from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same secret. Our solution can be executed in noisy, outdoor vehicular environments. We also propose an online parameter learning mechanism to adapt to different channel conditions. We conduct extensive realworld experiments to validate our solution.

Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by more than 10 times in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.

As the first step of the communication procedure in 802.11, an unwise selection of the access point (AP) hurts one client's throughput. This performance downgrade is usually hard to be offset by other methods, such as efficient rate adaptations. In this paper, we study this AP selection problem in a decentralized manner, with the objective of maximizing the minimum throughput among all clients. We reveal through theoretical analysis that the selfish strategy, which commonly applies in decentralized systems, cannot effectively achieve this objective. Accordingly, we propose an online AP association strategy that not only achieves a minimum throughput (among all clients) that is provably close to the optimum, but also works effectively in practice with reasonable computation and transmission overhead. The association protocol applying this strategy is implemented on the commercial hardware and compatible with legacy APs without any modification. We demonstrate its feasibility and performance through real experiments and intensive simulations.

Personal Health Records are starting to get more attention, as they have the potential to improve the quality of care and reduce healthcare costs. In this paper, the concepts behind PHRs and the advantages of using PHRs compared to other methods of medical record keeping are discussed. In particular, platform style PHRs that allow the easy incorporation of third party tools are examined, as they have the potential to allow medical data to be used in novel ways.

Remote obstetrics care monitoring is currently being used in many different countries to improve the quality of prenatal care, with promising results. The next generation of remote monitoring systems take advantage of improvements in wireless communications and mobile phone technologies to incorporate off-the-shelf equipment, such as Android smartphones, into their design. This not only reduces the overall cost, but also allows for greater flexibility, since the patient can perform monitoring in the comfort of their home. However, our analysis suggests that recently proposed systems have inadequate security protections needed to meet HIPAA requirements for health data. We also proposed recommendations to improve the security of these emerging systems.

Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In this paper, we address two fundamental issues in a cloud environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme proposed by Ostrovsky et. al. Then, based on an aggregation and distribution layer (ADL), we present a scheme, termed efficient information retrieval for ranked query (EIRQ), to further reduce querying costs incurred in the cloud. Queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. Extensive evaluations have been conducted on an analytical model to examine the effectiveness of our scheme.

Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by one to two orders of magnitude in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.

Cooperative trajectory mapping is an emerging technique that allows users to create a map by using data collected from each participant's mobile phones. Unlike the traditional localization problem, where GPS is usually required, cooperative mapping only requires information about the relative distance and direction from the previously reported position. In this paper, we consider the problem of measurement error, which is when the measurement error causes the spatial relations among users to be wrong, in cooperative trajectory mapping. We propose an encounter-based error canceling algorithm to efficiently reduce measurement errors. Extensive simulation experiments are performed to validate our solutions.

Map construction is an integral part of many location-based services. In this paper, we propose a feedback-based heuristic map construction algorithm (FHMCA). This is a lightweight, cooperative, map construction technique which can accurately capture the unique characteristics of each intersection and the length of every road without requiring the users to transmit large amounts of data or use GPS. The proposed algorithm improves the bandwidth and energy efficiency of cooperative map construction as no detailed maps are needed. However, the resulting map is still useful for location-based services. Moreover, our method is applicable when the existing of malicious users, who report wrong data. We validate the effectiveness of our solutions through extensive simulation experiments.

Migrating applications with strong consistency requirements to public cloud platforms remains risky since the data owner cannot verify the correctness of the public cloud's locking algorithm. In this paper, we identify new attacks that an untrusted cloud provider can launch via control of the locking mechanism, and propose an extension to existing locking scheme to address such attacks. Our solution modifies the locks to include a short history to allow data users to determine correctness, and can also prevent the cloud from re-ordering operations for financial gain.

Privacy is an important concern for location based services (LBSs). In this paper, we consider a specific type of LBS known as a mobile social network (MSN). We demonstrate a new type of attack, where an adversary can combine the location and friendship information found in a MSN, to violate user privacy. We propose a fake location reporting solution that does not require any additional trusted third party deployment. We use extensive simulations to determine the validity of our scheme.

A key approach to secure cloud computing is for the data owner to store encrypted data in the cloud, and issue decryption keys to authorized users. Then, when a user is revoked, the data owner will issue re-encryption commands to the cloud to re-encrypt the data, to prevent the revoked user from decrypting the data, and to generate new decryption keys to valid users, so that they can continue to access the data. However, since a cloud computing environment is comprised of many cloud servers, such commands may not be received and executed by all of the cloud servers due to unreliable network communications. In this paper, we solve this problem by proposing a time-based re-encryption scheme, which enables the cloud servers to automatically re-encrypt data based on their internal clocks. Our solution is built on top of a new encryption scheme, attribute-based encryption, to allow fine-grain access control, and does not require perfect clock synchronization for correctness.

This paper considers vehicular rogue access points (APs) that rogue APs are set up in moving vehicles to mimic legitimate roadside APs to lure users to associate to them. Due to its mobility, a vehicular rogue AP is able to maintain a long connection with users. Thus, the adversary has more time to launch various attacks to steal users' private information. We propose a practical detection scheme based on the comparison of Receive Signal Strength (RSS) to prevent users from connecting to rogue APs. The basic idea of our solution is to force APs (both legitimate and fake) to report their GPS locations and transmission powers in beacons. Based on such information, users can validate whether the measured RSS matches the value estimated from the AP's location, transmission power, and its own GPS location. Furthermore, we consider the impact of path loss and shadowing and propose a method based on rate adaption to deal with advanced rogue APs. We implemented our detection technique on commercial off-the-shelf devices including wireless cards, antennas, and GPS modules to evaluate the efficacy of our scheme.

Recent studies have revealed security vulnerabilities in implantable medical devices (IMDs). Security design for IMDs is complicated by the requirement that IMDs remain operable in an emergency when appropriate security credentials may be unavailable. In this paper, we introduce IMDGuard, a comprehensive security scheme for heart-related IMDs to fulfill this requirement. IMDGuard incorporates two techniques tailored to provide desirable protections for IMDs. One is an ECG based key establishment without prior shared secrets, and the other is an access control mechanism resilient to adversary spoofing attacks. The security and performance of IMDGuard are evaluated on our prototype implementation.

This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing-based technique that allows the user to avoid connecting to rogue APs. Our detection scheme is a client-centric approach that employs the round trip time between the user and the DNS server to independently determine whether an AP is a rogue AP without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach. The algorithm achieves close to 100 percent accuracy in distinguishing rogue APs from legitimate APs in lightly loaded traffic conditions, and larger than 60 percent accuracy in heavy traffic conditions. At the same time, the detection only requires less than 1 second for lightly-loaded traffic conditions and tens of seconds for heavy traffic conditions.

Radio Frequency IDentification (RFID) technology has attracted much attention due to its variety of applications, e.g., inventory control and object tracking. One important problem in RFID systems is how to quickly estimate the number of distinct tags without reading each tag individually. This problem plays a crucial role in many real-time monitoring and privacy-preserving applications. In this paper, we present an efficient and anonymous scheme for tag population estimation. This scheme leverages the position of the first reply from a group of tags in a frame. Results from mathematical analysis and extensive simulation demonstrate that our scheme outperforms other protocols proposed in the previous work.

In this paper we consider how to efficiently identify tags on the moving conveyor. Considering conditions like the path loss and multi-path effect in realistic settings, we first propose a probabilistic model for RFID tag identification. Based on this model, we propose efficient solutions to identify moving RFID tags, according to the fixed-path mobility on the conveyor. A dynamic program based solution and an adaptive solution are proposed to select optimized frame sizes during the query cycles. Simulation results indicate that by leveraging the probabilistic model our solutions can achieve much better performance than using parameters for the ideal propagation situations.

In a Wireless Local Area Network (WLAN), the Access Point (AP) selection of a client heavily influences the performance of its own and others. Through theoretical analysis, we reveal that previously proposed association protocols are not effective in maximizing the minimal throughput among all clients. Accordingly, we propose an online AP association strategy that not only achieves a minimal throughput (among all clients) that is provably close to the optimum, but also works effectively in practice with a reasonable computational overhead. The association protocol applying this strategy is implemented on the commercial hardware and compatible with legacy APs without any modification. We demonstrate its feasibility and performance through real experiments.

In this article, we present Microsearch, a search system suitable for embedded devices used in ubiquitous computing environments. Akin to a desktop search engine, Microsearch indexes the information inside a small device, and accurately resolves a user's queries. Given the limited hardware, conventional search engine design and algorithms cannot be used. We adopt Information Retrieval (IR) techniques for query resolution, and proposed a new space-efficient top- k query resolution algorithm. A theoretical model of Microsearch is given to better understand the trade-offs in design parameters. Evaluation is done via actual implementation on off-the-shelf hardware.

Embedding small devices into everyday objects like toasters and coffee mugs creates a wireless network of objects. These embedded devices can contain a description of the underlying objects, or other user defined information. In this paper, we present Snoogle, a search engine for such a network. A user can query Snoogle to find a particular mobile object, or a list of objects that fit the description. Snoogle uses information retrieval techniques to index information and process user queries, and Bloom filters to reduce communication overhead. Security and privacy protections are also engineered into Snoogle to protect sensitive information. We have implemented a prototype of Snoogle using off-the-shelf sensor motes, and conducted extensive experiments to evaluate the system performance.

As RFID tags become more widespread, new approaches for managing larger numbers of RFID tags will be needed. In this paper, we consider the problem of how to accurately and efficiently monitor a set of RFID tags for missing tags. Our approach accurately monitors a set of tags without collecting IDs from them. It differs from traditional research which focuses on faster ways for collecting IDs from every tag. We present two monitoring protocols, one designed for a trusted reader and the other for an untrusted reader.

This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing based technique that allows the user to avoid connecting to rogue APs. Our method employs the round trip time between the user and the DNS server to independently determine whether an AP is legitimate or not without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance.

A body sensor network (BSN) is a network of sensors deployed on a person's body for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a BSN. In this paper, we developed IBE-Lite, a lightweight identity-based encryption suitable for sensors in a BSN. We present protocols based on IBE-Lite that balance security and privacy with accessibility and perform evaluation using experiments conducted on commercially available sensors.

As RFID tags become more widespread, new approaches for managing larger numbers of RFID tags will be needed. In this paper, we consider the problem of how to accurately and efficiently monitor a set of RFID tags for missing tags. Our approach accurately monitors a set of tags without collecting IDs from them. It differs from traditional research which focuses on faster ways for collecting IDs from every tag. We present two monitoring protocols, one designed for a trusted reader and another for an untrusted reader.

While symmetric-key schemes are efficient in processing time for sensor networks, they generally require complicated key management, which may introduce large memory and communication overhead. On the contrary, public-key based schemes have simple and clean key management, but cost more computational time. The recent progress of elliptic curve cryptography (ECC) implementation on sensors motivates us to design a public-key scheme and compare its performance with the symmetric-key counterparts. This paper builds the user access control on commercial off-the-shelf sensor devices as a case study to show that the public-key scheme can be more advantageous in terms of the memory usage, message complexity, and security resilience. Meanwhile, our work also provides insights in integrating and designing public-key based security protocols for sensor networks.

Hardware advances will allow us to embed small devices into everyday objects such as toasters and coffee mugs, thus naturally form a wireless object network that connects the object with each other. This paper presents Snoogle, a search engine for such a network. Snoogle uses information retrieval techniques to index information and process user queries, and compression schemes such as Bloom filters to reduce communication overhead. Snoogle also considers security and privacy protections for sensitive data. We have implemented the system prototype on off-the-shelf sensor motes, and conducted extensive experiments to evaluate the system performance.

As RFID tags are increasingly attached to everyday items, it quickly becomes impractical to collect data from every tag in order to extract useful information. In this paper, we consider the problem of identifying popular categories of RFID tags out of a large collection of tags, without reading all the tag data. We propose two algorithms based on the idea of group testing, which allows us to efficiently derive popular categories of tags. We evaluate our solutions using both theoretical analysis and simulation.

A body sensor network (BSN), is a network of sensors deployed on a person's body, usually for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a body sensor network. At the same time, the collected data has to readily available in the event of an emergency. In this paper, we present IBE-Lite, a lightweight identity-based encryption suitable for sensors, and developed protocols based on IBE-Lite for a BSN.

With the increased popularity of RFID applications, different authentication schemes have been proposed to provide security and privacy protection for users. Most recent RFID protocols use a central database to store the RFID tag data. The RFID reader first queries the RFID tag and returns the reply to the database. After authentication, the database returns the tag data to the reader. In this paper, we propose a more flexible authentication protocol that provides comparable protection without the need for a central database. We also suggest a protocol for secure search for RFID tags. We believe that as RFID applications become widespread, the ability to securely search for RFID tags will be increasingly useful.

With the increasing popularity of RFID applications, different authentication schemes have been proposed to provide security and privacy protection to users. Most recent RFID protocols use a central database to store the RFID tag data. An RFID reader first queries the RFID tag and returns the reply to the database. After authentication, the database returns the tag data to the reader. In this paper, we proposed a more flexible authentication protocol that provides comparable protection without the need for a central database. We also suggest a protocol for secure search for RFID tags. We believe that as RFID applications become widespread, the ability to search for RFID tags will be increasingly useful