Recent hardware and software advances allow efficient local machine learning inference on edge devices such as smart cameras. This feature addresses users’ privacy concerns, as they no longer need to upload sensitive raw data, such as images and videos, to cloud servers for data analysis. This paper studies potential privacy breaches in this emerging computing paradigm. Specifically, we show that it is possible to manipulate production neural network models to turn them into spyware. The inference results of these models are almost the same as they are expected to be, but can convey information about the raw input content, allowing the curious cloud service provider managing the edge devices to covertly spy on the private information without being noticed. In the end, we discuss possible defense approaches.

The increasing use of camera streams on mobile systems has raised significant privacy concerns due to unauthorized visual data access by applications. Existing solutions either burden users with excessive interaction or lack semantic understanding of contextual privacy norms. This paper introduces PrivacyAgent, a novel visual privacy protection framework leveraging multimodal large language models (LLMs) to enable context-aware and fine-grained privacy control on mobile systems. PrivacyAgent intercepts camera streams via a virtualized I/O layer and restricts untrusted apps to privacy-compliant content with minimal user overhead.

Fingerprint scanners are widely used in security applications. In this work, we demonstrate that capacitive fingerprint scanners can also serve as universal communication devices. In this demonstration, we show that a reliable data transmission channel can be established between a wearable device and the fingerprint scanner, using the human body as the medium. We also envision several promising applications that arise from this new opportunity.

As assisted and autonomous driving systems become more prevalent, the need for accurate interpretation of road traffic signs is critical for driving safety and functionality. Current camera-based recognition methods face challenges due to the variability of traffic signs and environmental conditions, leading to potential inaccuracies. To address this, we propose LiDARMarker, a type of machine-readable traffic sign using infrared materials, making it invisible to human drivers but detectable by LiDAR-equipped vehicles. This paper introduces the design, fabrication, and efficient decoding methods of LiDARMarker. LiDARMarker is tailored to the emerging capabilities and needs of modern vehicles, enhancing their ability and accuracy in traffic sign recognition while avoiding interference with human drivers. Through the proposal of LiDARMarker, we aim to inspire the rethinking of the design of traffic sign systems in the context of modern vehicles.

In this paper, we propose an innovative method for fingerprinting USB peripherals. While USB technology has made significant progress in data transfer efficiency, by default, the USB host device trusts any connected peripherals. This ignorance has led to several security risks in practice. Existing protection practices mitigate these risks by verifying the specific type or even the identity of the peripheral before data transfer. However, these methods often depend on expensive and specialized hardware, such as software-defined radios, limiting their applicability in everyday scenarios.To address this issue, we propose profiling the electromagnetic radiation (EMR) generated by a USB peripheral as its unique identifier. Our method utilizes a low-cost backscatter unit and the host’s WiFi network card for the profiling and verification process. The backscatter unit is integrated into a USB hub. It shifts the USB EMR signal into the frequency domain within the WiFi sensing range, and transforms EMR into an interpretable form suitable for feature extraction. Subsequently, we employ a hybrid classification method, combining heuristic and neural network clues, to recognize the identity of the EMR’s source. We evaluate the effectiveness of our method on an extensive dataset collected from USB peripherals of various types and vendors.

Wi-Fi sensing technology can infer information about the environment, including user privacy. With the evolution of the smart home, more and more Wi-Fi devices are placed in a home. The ubiquitous Wi-Fi devices lead to concerns over privacy breaches caused by Wi-Fi sensing. In this paper, we discuss the insider threat model, where the smart device may be used to spy on the user through Wi-Fi sensing. To protect user privacy in the insider model, we propose WiShield, a bolt-on countermeasure against abusing smart devices for malicious Wi-Fi sensing in a per-device manner. WiShield works by attaching it to the smart devices. It utilizes low-cost radio frequency (RF) components to obfuscate the smart devices’ sensing by switching among multiple propagation paths with different gains and lengths. The user can recover a certain device’s sensing by disabling the corresponding WiShield through a policy configuration or command. We validate the effectiveness of WiShield against two state-of-the-art sensing strategies. The result shows that WiShield is effective in hiding most information contained in the Wi-Fi signals.

The privacy of fingerprints is a growing concern due to the risk of data breaches and subsequent attacks. A key issue is that the information of the same fingerprint may exist on multiple devices, and device-level protection mechanisms are fundamentally limited in their coverage. Consequently, information leakage from any device potentially affects all enrolled fingerprint recognition devices. In this paper, we introduce a novel fingerprint enrollment method called MaskPrint, which allows users to enroll in various fingerprint recognition systems using distinct information. This approach can largely mitigate the risk of data breaches, providing a user-transparent, device-agnostic fingerprint protection measure. Our method involves collecting the original fingerprint information, selecting a minimum feature set, synthesizing protective fingerprints, and fabricating physical ones for enrollment. Users can complete these procedures on their own. We validate the effectiveness and usability of MaskPrint on commercial fingerprint recognition systems.

Remote conference applications are increasingly widely used, but currently, their improper data encryption methods, proprietary implementations, and dial-in access cause concerns about privacy breaches. As such, there is a need for trustworthy and secure solutions for these production tools. In this paper, we present mTunnel, a transparent software layer in the host system for securing conference applications without sacrificing the key functionalities and convenience. The basic idea of mTunnel is to encrypt sensitive data, such as audio, video, text, etc., before it is obtained by untrusted application clients. mTunnel leverages the audio and video streaming capabilities of the conference applications to tunnel the encrypted content to the remote end. mTunnel involves a software framework to accommodate the media interception and representation through I/O virtualization based on virtual drivers. Moreover, mTunnel supports complete E2EE group conversations even in a mixed IP and public switched telephone network (PSTN). We implement mTunnel and evaluate it with several commercial products. Results show its feasibility and overhead.

Facial information has been used for authentication purposes. Recent face authentication systems leverage multi-modal cameras to defeat spoofing attacks. Multimodal cameras are able to simultaneously observe the targeting people from multiple physical aspects, such as the visible, infrared, and depth domains. Known spoofing attacks are not effective in evading the detection since they cannot simulate multiple modalities at the same time.This paper presents a new class of spoofing attacks on multimodal face authentication systems. Its main idea is to forge each and every modality and then combine them together to present to the camera. The attack is realized with a special display device called Hua-pi display. It costs less than $500 and incorporates dedicated scene generators to optically reproduce multimodal scenes of an authorized user, and then synthesizes the scenes together at the camera’s view point through optical combiners to fool face authentication systems. We evaluate the risks of this attack by systematically testing it against the latest commercial face authentication products from major vendors in the field. The results not only demonstrate a successful bypass rate of 80% but also characterize the impacting factors and their feasible regions, revealing a new and realistic threat in the field.

This paper presents MagCode, a new type of barcode. MagCode label is printed with magnetic ink through ordinary printers and stores data in the magnetic field. By swiping a magnetometer over it, the stored data can be extracted. Thus, unlike ordinary barcodes, MagCode does not rely on visual channels to decode, implying untapped interaction opportunities when decoding ordinary visual code is not preferred or not possible. Its application cases include an unobstructed and low-cost input interface for wearables, whose visual sensing capabilities are usually limited. Further, MagCode's patterns can be designed to be visually-undecodable and our evaluation shows that accessing its magnetic signal requires very close approximation. These properties allow for using MagCode labels to convey covert and sensitive information, such as Easter eggs in games and payment credentials in daily life.

Emerging mobile applications involving distributed control and sensing call for accurate time synchronization over wireless links. This paper systematically studies the performance of Precision Time Protocol (PTP) in today’s Wi-Fi networks. We investigate both software and hardware PTP implementations. Our study uncovers the root causes of software PTP synchronization errors. We show that with fine-tuned system configurations and an online calibration procedure, software PTP can achieve reasonable accuracy with off-the-shelf Wi-Fi devices. Hardware PTP requires a PTP hardware timestamper clock not contained in Wi-Fi NICs. We propose a method to make use of the hardware TSF counter to emulate the PTP clock. Rigorous tests traversing various conditions show that both software and hardware PTP implementations can achieve 1-$\mu s$level accuracy on current ARM and x86 mobile platforms with practical Wi-Fi settings.

This paper presents Centaur, an input system that enables tangible interaction on displays, e.g., untouchable computer monitors. Centaur’s tangibles are built from low-cost optical mouse sensors, or can alternatively be emulated by commercial optical mice already available. They are trackable when put on the display, rendering a real-time and high-precision tangible interface. Even for ordinary personal computers, enabling Centaur requires no new hardware and installation burden. Centaur’s cost-effectiveness and wide availability open up new opportunities for tangible user interface (TUI) users and practitioners. Centaur’s key innovation lies in its tracking method. It embeds high-frequency light signals into different portions of the display content as location beacons. When the tangibles are put on the screen, they are able to sense the light signals with their optical mouse sensors, and thus determine the locations accordingly. We develop four applications to showcase the potential usage of Centaur.

QUIC's connection migration provides an opportunity for network middleboxes to control paths of network traffic in a fine time granularity without worrying about breaking down the transport layer connections. This paper takes the first step to investigate its feasibility and effectiveness. We apply connection migration to a multi-carrier Wi-Fi relay to counter the latency caused by cellular network suspension. We identify a deadlock in such a situation, which eliminates the gain of passive migration. We analyze the root cause and propose a simple but effective approach to resolve the deadlock. Our findings may also motivate similar applications in other middlebox scenarios.

Smart speakers play an important role in smart home envision. Active acoustic sensing can enable convenient gesture interaction on smart speakers to complement voice interaction in mandatory quiet scenarios and address privacy concerns. However, existing solutions did not consider the impact of the widely adopted uniform circular geometry of commercial smart speakers on gesture tracking. To fill this gap, we propose SparseTrack to achieve fine-grained multi-user device-free gesture tracking on commercial smart speakers. We cast gesture tracking to sparse recovery intuition to address signal coherence issue on uniform circular mic-array. We then synthesize wideband measurement to eliminate spatial ambiguity caused by the insufficient spatial sampling rate of today’s smart speakers in the ultrasonic frequency band. We further design a robust trace extraction approach and properly handle the impact of the doppler effect on gesture tracking. We implement SparseTrack on COTS circular mic-array and conduct extensive evaluations. The results show that our system can simultaneously track up to 4 users’ gestures with a mean tracking error of 2.66 cm.

This paper proposes MousePath, a novel lightweight communication system between PC web pages and smartphones. MousePath works by putting the optical mouse on top of the smartphone's screen, then its transmission starts and is instantly finished without association and pairing friction. It encodes data into the movement of smartphone's display content and leverages the optical mouse of the computer to sense the movement for decoding the data. We prototype and evaluate the system with commercial computers and smartphones. A key benefit of MousePath is that it can be seamlessly integrated into web pages. Two representative web applications, i.e., sensor sharing and message sharing, have been developed to demonstrate MousePath's potential in enhancing PC web page functionalities.

Visible light positioning (VLP) is a promising direction for indoor localization. VLP depends on Visible light communication (VLC) to receive location anchors sent by light bulbs. In order to decode high-frequency VLC signals, today's VLP systems require the receiver to equip either rolling shutter cameras or high-frequency light sensors, which bring considerable overhead or are even unavailable on many mobile devices. This paper introduces ALS-P, a lightweight VLP approach which only requires the commercially widely available ambient light sensor (ALS). ALS is conventionally not treated as a feasible VLC receiver as its sampling rate is far less than that of VLC signals. Our basic idea is to leverage the property of frequency aliasing. Through dynamically adjusting the sampling rate of the ALS sensor, the down-converted signals can be uniquely distinguished. To realize this idea, we propose novel designs to address challenges which stem from ALS hardware, high-order light aliasing, and environmental interference cancellation. Besides, ALS can also enable a lightweight VLC via changing LED frequencies. We implement the ALS-P on commercial LED bulbs and the ALS of existing smartphones. The evaluation shows that the ALSP can enable robust and efficient LED frequency decoding with at least 4LEDs bulbs in practical scenarios. As a result, ALS-P can enable VLC at a data rate of 5bit/s per each LED bulb and achieve a sub-meter level indoor localization accuracy.

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden for receiving with cameras. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for devices with a camera. This paper presents the VLP system PIXEL, which realizes our idea. In PIXEL, we developed 1) a novel color-based modulation scheme to handle user's mobility and 2) a sensor-assisted positioning algorithm to ease user's burden in capturing multiple location anchors. Our experiments based on the prototype show that PIXEL can provide 10 cm-level real-time VLP for wearables and smartphones with camera resolution as coarse as 60 pixel × 80 pixel and CPU frequency as low as 300 MHz.

This paper presents DriveSafe Inspector, a fine-grained driver hand position monitoring system, which continuously detects a driver's hand position on the steering wheel. The steering wheel is divided into twelve 30° sectors like a clock. Our system can be applied on off-the-shelf hardware and works without extra modification to vehicles. In our system, sensor readings from both a wearable and its paired smartphone are fused to infer hand posture and turning angle between static holding states. With both static holding and dynamic turning information, our system achieves fine-grained hand position prediction in the presence of diverse road conditions and inter-individual differences. The on-road evaluation shows that our system can achieve an average 91.59% hand position detection accuracy with only static information, and can be further improved to 94.63% accuracy combined with dynamic turning information.

Mobile sensing enables unobtrusive monitoring of our daily activities, sleep quality, breathing and heart rate, revolutionizing the health-care system. Dietary information is also a critical dimension for health management but has no convenient solution yet. In this paper, we ask whether we can track meal composition unobtrusively. We introduce Smart-U, a new utensil design that can recognize meal composition during the intake process, without user intervention or on-body instruments. Smart-U makes use of the fact that light spectra reflected by foods are dependent on the food ingredients. By analyzing the reflected light spectra, Smart-U can recognize what food is on top of the utensil. We describe the prototype design of Smart-U and the food recognition algorithm. We demonstrate that Smart-U can recognize 20 types of foods with 93 % accuracy. It can work robustly under different conditions. We envision that Smart-U can enable automatic food intake tracking, and provide personalized food suggestions based on nutrient recommendations and prior consumption. In the long run, Smart-U can contribute to the study of chronic diseases.

Nowadays, wrist wearables are widely used in applications containing privacy sensitive data. Protecting these data from unauthorized access is highly demanded. However, Personal Identification Number(PIN)-based certificate is not convenient to use for wearables are small in size and have no/very limited input ways. Like signing on paper, signing a pattern in the air and authenticate the user according to the trait from motion sensors is a potential solution for the authentication needs on wearables. However, we observe the successful authentication rate of such approaches in existing designs is not high enough under different postures. We identify the problem is caused by the residue gravity acceleration in motion sensors which introduces inaccuracy in capturing signing dynamic. To this end, this paper proposes a robust gesture-based authentication method that can be used under different postures. Our key idea to use and only use the gyroscope sensor to capture the signing dynamics, so that to get rid of the impact from gravity. To realize this idea, we propose an accurate wrist dynamic capturing method with gyroscope sensor, through which behavior traits are extracted from both time and shape domain. Finally, by comparing the Dynamic Time Warping distance between the real time input and the traits template, signing user can be identified. We implement our method on android smart watch. Our evaluation involves 11 users and lasts for more than one month. The results show that our method can achieve 90.1% balanced accuracy and have stable performance under different postures.

Internet of Things (IoT) devices are largely embedded devices which lack a sophisticated user interface, e.g., touch screen, keyboard, etc. As a consequence, traditional Pre-Shared Key (PSK) based authentication for mobile devices becomes difficult to apply. For example, according to our study on home automation devices which leverage smartphone for PSK input, the current process does not protect against active impersonating attack and also leaks the Wi-Fi password to eavesdroppers, i.e., currently these IoT devices can be exploited to enter into critical infrastructures, e.g., home networks. Motivated by this real-world security vulnerability, in this paper we propose a novel proximity-based mechanism for IoT device authentication, called Move2Auth, for the purpose of enhancing IoT device security. In Move2Auth, we require user to hold smartphone and perform one of two hand-gestures (moving towards and away, and rotating) in front of IoT device. By combining (1) large RSS-variation and (2) matching between RSS-trace and smartphone sensor-trace, Move2Auth can reliably detect proximity and authenticate IoT device accordingly. Based on our implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, we prove Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.

Today's mobile devices contain sensitive data, which raises concerns about data security. This paper discusses a covert channel threat on existing mobile systems. Through it, malware can wirelessly leak information without making network connections or emitting signals, such as sound, EMR, vibration, etc., that we can feel or are aware of. The covert channel is built on a communication method that we call NICScatter. NICScatter transmitter malware forces mobile devices, such as mobile phones, tablets or laptops, to reflect surrounding RF signals to covertly convey information. The operation is achieved by controlling the impedance of a device's wireless network interface card (NIC). Importantly, the operation requires no special privileges on current mobile OSs, which allows the malware to stealthily pass sensitive data to an attacker's nearby mobile device, which can then decode the signal and thus effectively gather the guarded data. Our experiments with different mobile devices show that the covert channel can achieve 1.6 bps and transmit as far as 2 meters. In a through-the-wall scenario, it can transmit up to 70 cm.

This paper presents Shadow, a novel display-to-device communication system working in radio frequency. It leverages the Electromagnetic Radiation (EMR) emanated from display to transmit information. We modulate the signal transmitted to display to make the corresponding EMR signal fall into the FM band. In this way. when users' devices with FM ability are approaching to the display, it can automatically receive information from the display. Since Shadow does not rely on camera, wearable devices can also obtain information from display. In addition to the lightweight communication ability, Shadow keeps the communication truly invisible by only transmitting in the period that will not be shown on the display panel. Our system requires no modification on hardware, and we implement it with commodity display system and mobile devices. Results show it can achieve 1.5 kbps at distances of up to 20cm from the display panel.

Performance anomaly is a well-known problem in 802.11 networks. It refers to the case where the aggregated throughput of the multirate network is degraded by slow rate links due to the fair sharing of transmission opportunities. As a result of growing heterogeneity in 802.11 hardware, the coexistence of slow rate and fast rate links are more common and performance anomaly is considered severer. In this paper, we propose to leverage the emerging full duplex technology to relief the penalty of performance anomaly for 802.11 network. Although in general cases, full duplex gain is limited by the uplink-downlink interference (UDI), we find in performance anomaly, it is possible to eliminate UDI and achieve full duplex communication in slow rate links through successive interference cancellation (SIC). To realize such idea, we design a novel MAC protocol for full duplex AP and half duplex clients. When performance anomaly occurs, the protocol leverages a distributed pairing algorithm to select slow rate links for full duplex communication to increase the network throughput. We implement the full MAC design in NS3 network simulator, results show it has an average throughput gain of 30% over legacy protocol.

This paper studies the feasibility of using mobile phone to achieve low-cost and accurate three-dimensional (3D) road modeling, which provides nutritious supplement to current digital map. Instead of using expensive tools, our method only needs a mobile phone fixed on a moving vehicle. Road surface variation, reflected by vehicle dynamic state, is captured by sensors in the mobile phone.The road in 3D is reproduced by combining sensing data from accelerometer, magnetometer, gyroscope, and GPS trace. Several compensation and calibration techniques are developed to improve modeling accuracy based on the systematic study of sensor error properties. A learning step is used to automatically unify the user dependent factors to avoid manual adjustment. We evaluate our method on a 2 km real road. Compared with the field measured baseline, it successfully generates a fine-grained 3D road model with maximum height error less than 4 m and 90 degree slope angle error less than 2 degrees.

Today's WLANs are struggling to provide desirable features like high efficiency, fairness and QoS because of the use of Distributed Coordination Function (DCF). In this paper we present OpenTDMF, an architecture to enable TDMA on commodity WLAN devices. Our hope is to provide the desirable features without entirely rebuilding the WLAN infrastructure. OpenTDMF is inspired by and architecturally similar to Software Defined Networking (SDN). Specifically, we leverage the backhaul of WLAN to coordinate all the stations for channel access. This fine-grained coordination is performed in a decoupled control plane which includes a central controller and programmable APs. To realize OpenTDMF on commodity WLAN devices, we develop several novel techniques to achieve μs-level time synchronization among all the APs. We also enable AP-triggered uplink transmission so that all the transmissions in the WLAN can be determined. We implemented a prototype of OpenTDMF based on commodity WLAN devices. Empirical results validate the OpenTDMF design and demonstrate its benefits.

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls.

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls.

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden to receiving devices. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for resource-constrained wearable devices, e.g. smart glasses. Moreover, the polarization-based VLC can be applied to any illuminating light sources, thereby eliminating the dependency on LED.

Existing work on indoor localization achieves accuracy by conducting site surveys or deploying additional infrastructures. In this paper, we study the feasibility of designing a low cost scheme, which is based on pedestrian dead reckoning and free from these requirements. Our method targets the scenarios where there are a large number of pedestrians in the public places holding their mobile phones while walking. First, we make one key observation that positioning errors brought by different pedestrians are diverse and can be mutually compensated. Second, we leverage the acoustic communication ability in mobile phone to enable users to exchange their locations when they meet with each other within a short distance. The received location information is crowdsourced to get a better location estimate. Finally, we use extensive experiments to evaluate the proposed scheme; the experiments are conducted in a 3,500 square-meter area with more than 18 hours (more than 100 km) real walking traces. Results show that our method is able to reduce the median localization error to 4 m when the user density of the area is more than 50/3,500 per square-meter.