Web performance anomalies (e.g. time periods when metrics like page load time are abnormally high) have significant impact on user experience and revenues of web service providers. Existing methods to automatically detect web performance anomalies focus on popular websites (e.g. with tens of thousands of visits per minute). Across a wider diversity of websites, however, the number of visits per hour varies enormously, and some sites will only have few visits per hour. Low rates of visits create measurement gaps and noise that prevent the use of existing methods. This paper develops WMF, a web performance anomaly detection method applicable across a range of websites with highly variable measurement volume. To demonstrate our method, we leverage data from a website monitoring company, which allows us to leverage cross-site measurements. WMF uses matrix factorization to mine patterns that emerge from a subset of the websites to "fill in" missing data on other websites. Our validation using both a controlled website and synthetic anomalies shows that WMF's F1-score is more than double that of the state-of-the-art method. We then apply WMF to three months of web performance measurements to shed light on performance anomalies across a variety of 125 small to medium websites.

The current Internet content delivery model assumes strict mapping between a resource and its descriptor, e.g., a JPEG file and its URL. Content Distribution Networks (CDNs) extend it by replicating the same resources across multiple locations, and introducing multiple descriptors. The goal of this work is to build Web-LEGO, an opt-in service, to speedup webpages at client side. Our rationale is to replace the slow original content with fast similar or equal content. Further, we perform a reality check of this idea both in term of the prevalence of CDN-less websites, availability of similar content, and user perception of similar webpages via millions of scale automated tests and thousands of real users. Then, we devise Web-LEGO, and address natural concerns on content inconsistency and copyright infringements. The final evaluation shows that Web-LEGO brings significant improvements both in term of reduced Page Load Time (PLT) and user-perceived PLT. Specifically, CDN-less websites provide more room for speedup than CDN-hosted ones, i.e., 7x more in the median case. Besides, Web-LEGO achieves high visual accuracy (94.2%) and high scores from a paid survey: 92% of the feedback collected from 1,000 people confirm Web-LEGO's accuracy as well as positive interest in the service.

Website fingerprinting (WFP) could infer which websites a user is accessing via an encrypted proxy by passively inspecting the traffic between the user and the proxy. The key to WFP is designing a classifier capable of distinguishing traffic characteristics of accessing different websites. However, when deployed in real-life networks, a well-trained classifier may face a significant obstacle of training-testing asymmetry, which fundamentally limits its practicability. Specifically, although pure traffic samples can be collected in a controlled (clean) testbed for training, the classifier may fail to extract such pure traffic samples as its input from raw complicated traffic for testing. In this paper, we are interested in encrypted proxies that relay connections between the user and the proxy individually (e.g., Shadowsocks), and design a context-aware system using built-in spatial-temporal flow correlation to address the obstacle. Extensive experiments demonstrate that our system does not only enable WFP against a popular type of encrypted proxies practical, but also achieves better performance than ideally training/testing pure samples.

Current web tracking practices pose a constant threat to the privacy of Internet users. As a result, the research community has recently proposed different tools to combat well-known tracking methods. However, the early detection of new, previously unseen tracking systems is still an open research problem. In this paper, we present TrackSign, a novel approach to discover new web tracking methods. The main idea behind TrackSign is the use of code fingerprinting to identify common pieces of code shared across multiple domains. To detect tracking fingerprints, TrackSign builds a novel 3-mode network graph that captures the relationship between fingerprints, resources and domains. We evaluated TrackSign with the top-100K most popular Internet domains, including almost 1M web resources from more than 5M HTTP requests. Our results show that our method can detect new web tracking resources with high precision (over 92%). TrackSign was able to detect 30K new trackers, more than 10K new tracking resources and 270K new tracking URLs, not yet detected by most popular blacklists. Finally, we also validate the effectiveness of TrackSign with more than 20 years of historical data from the Internet Archive.

Device-free acoustic sensing has obsessed with renovating human-computer interaction techniques for all-sized mobile devices in various applications. Recent advances have explored sound signals in different methods to achieve highly accurate and efficient tracking and recognition. However, accuracies of most approaches remain bottlenecked by the limited sampling rate and narrow bandwidth, leading to restrictions and inconvenience in applications. To bridge over the aforementioned daunting barriers, we propose PDF, a novel ultrasound-based device-free tracking scheme that can distinctly improve the resolution of fine-grained sensing to submillimetre level. In its heart lies an original Phase Difference based approach to derive time delay of the reflected Frequency-Modulated Continuous Wave (FMCW), thus precisely inferring absolute distance, catering to interaction needs of tinier perception with lower delay. The distance resolution of PDF is only related to the speed of actions and chirp duration. We implement a prototype with effective denoising methods all in the time domain on smartphones. The evaluation results show that PDF achieves accuracies of 2.5 mm, 3.6 mm, and 2.1 mm in distance change, absolute distance change, and trajectory tracking error respectively. PDF is also valid in recognizing 2 mm or even tinier micro-movements, which paves the way for more delicate sensing work.

UHF RFID technology becomes increasingly popular in RFID-enabled stores (e.g., UNIQLO), since UHF RFID readers can quickly read a large number of RFID tags from afar. The deployed RFID infrastructure, however, does not directly benefit smartphone users in the stores, mainly because smart-phones cannot read UHF RFID tags or fetch relevant information (e.g., updated price, real-time promotion). This paper aims to bridge the gap and allow users to 'read' UHF RFID tags using their smart-phones, without any hardware modification to either deployed RFID systems or smartphone hardware. To 'read' an interested tag, a user makes a pre-defined smartphone gesture in front of an interested tag. The smartphone gesture causes changes in 1) RFID measurement data (e.g., phase) captured by RFID infrastructure, and 2) motion sensor data (e.g., accelerometer) captured by the user's smartphone. By matching the two data, our system (named ShakeReader) can pair the interested tag with the corresponding smartphone, thereby enabling the smartphone to indirectly 'read' the interested UHF tag. We build a novel reflector polarization model to analyze the impact of smartphone gesture to RFID backscattered signals. Experimental results show that ShakeReader can accurately pair interested tags with their corresponding smart-phones with an accuracy of >94.6%.

Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation workloads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1% average latency than the baseline solution.

Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacking data and models, a convenient approach is needed which enables the trained models from various sources for prediction serving, but it has yet to truly take off considering three issues: (i) incentivizing prediction truthfulness; (ii) boosting prediction accuracy; (iii) protecting model privacy. We design FedServing, a federated prediction serving framework, achieving the three issues. First, we customize an incentive mechanism based on Bayesian game theory which ensures that joining providers at a Bayesian Nash Equilibrium will provide truthful (not meaningless) predictions. Second, working jointly with the incentive mechanism, we employ truth discovery algorithms to aggregate truthful but possibly inaccurate predictions for boosting prediction accuracy. Third, providers can locally deploy their models and their predictions are securely aggregated inside TEEs. Attractively, our design supports popular prediction formats, including top-1 label, ranked labels and posterior probability. Besides, blockchain is employed as a complementary component to enforce exchange fairness. By conducting extensive experiments, we validate the expected properties of our design. We also empirically demonstrate that FedServing reduces the risk of certain membership inference attack.