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Wearable devices especially smartwatches are widely used in our daily life. With their increased use, a large amount of sensitive data are collected, stored, and managed in those devices. To protect sensitive data, encryption is often used but, traditional encryption is vulnerable to a novel coercive attack in which the adversary can capture the device’s user and coerce the user to disclose the decryption key. To defend against the coercive attack, Plausibly Deniable Encryption (PDE) has been designed which can allow the victim user to deny the existence of hidden sensitive data. The PDE systems have been explored broadly for smartphones. However, the PDE systems which are suitable for wearable devices are still missing in the literature. In this work, we have designed MobiWear, the first PDE system specifically designed for wearable devices. By leveraging PDE, image steganography as well as watermarking, MobiWear ensures plausible deniability and can be easily deployed at the application layer. In addition, MobiWear relies on sensors equipped with the wearable devices to enter passwords, accommodating the wearable devices which have small-size screens and are inconvenient for entering plaintext. Security analysis and experimental evaluation using a real-world prototype (ported to an LG G smartwatch) show that MobiWear can ensure the deniability with a small computational overhead as well as a tiny degradation of the perceived quality of the image.

 

In today's digital landscape, the ubiquity of mobile devices underscores the urgent need for stringent security protocols in both data transmission and storage. Plausibly deniable encryption (PDE) stands out as a pivotal solution, particularly in jurisdictions marked by rigorous regulations or increased vulnerabilities of personal data. However, the existing PDE systems for mobile platforms have evident limitations. These include vulnerabilities to multi-snapshot attacks over RAM and flash memory, an undue dependence on non-secure operating systems, traceable PDE entry point, and a conspicuous PDE application prone to reverse engineering. To address these limitations, we have introduced FSPDE, the first Full-Stack mobile PDE system design which can mitigate PDE compromises present at both the execution and the storage layers of mobile stack as well as the cross-layer communication. Utilizing the resilient security features of ARM TrustZone and collaborating multiple storage sub-layers (block device, flash translation layer, etc.), FSPDE offers a suite of improvements. At the heart of our design, the MUTE and MIST protocols serve both as fortifications against emerging threats and as tools to mask sensitive data, including the PDE access point. A real-world prototype of FSPDE was developed using OP-TEE, a leading open-source Trusted Execution Environment, in tandem with an open-sourced NAND flash controller. Security analysis and experimental evaluations justify both the security and the practicality of our design. To address these limitations, we have introduced FSPDE, the first Full-Stack mobile PDE system design which can mitigate PDE compromises present at both the execution and the storage layers of mobile stack as well as the cross-layer communication. Utilizing the resilient security features of ARM TrustZone and collaborating multiple storage sub-layers (block device, flash translation layer, etc.), FSPDE offers a suite of improvements. At the heart of our design, the MUTE and MIST protocols serve both as fortifications against emerging threats and as tools to mask sensitive data, including the PDE access point. A real-world prototype of FSPDE was developed using OP-TEE, a leading open-source Trusted Execution Environment, in tandem with an open-sourced NAND flash controller. Security analysis and experimental evaluations justify both the security and the practicality of our design. 

Traditional encryption methods cannot defend against coercive attacks in which the adversary captures both the user and the possessed computing device, and forces the user to disclose the decryption keys. Plausibly deniable encryption (PDE) has been designed to defend against this strong coercive attacker. At its core, PDE allows the victim to plausibly deny the very existence of hidden sensitive data and the corresponding decryption keys upon being coerced. Designing an efficient PDE system for a mobile platform, however, is challenging due to various design constraints bound to the mobile systems. Leveraging image steganography and the built-in hardware security feature of mobile devices, namely TrustZone, we have designed a Simple Mobile Plausibly Deniable Encryption (SMPDE) system which can combat coercive adversaries and, meanwhile, is able to overcome unique design constraints. In our design, the encoding/decoding process of image steganography is bounded together with Arm TrustZone. In this manner, the coercive adversary will be given a decoy key, which can only activate a DUMMY trusted application that will instead sanitize the sensitive information stored hidden in the stego-image upon decoding. On the contrary, the actual user can be given the true key, which can activate the PDE trusted application that can really extract the sensitive information from the stego-image upon decoding. Security analysis and experimental evaluation justify both the security and the efficiency of our design. 

Autonomous mobile robots (AMRs) have the capability to execute a wide range of tasks with minimal human intervention. However, one of the major limitations of AMRs is their limited battery life, which often results in interruptions to their task execution and the need to reach the nearest charging station. Optimizing energy consumption in AMRs has become a critical challenge in their deployment. Through empirical studies on real AMRs, we have identified a lack of coordination between computation and control as a major source of energy inefficiency. In this paper, we propose a comprehensive energy prediction model that provides real-time energy consumption for each component of the AMR. Additionally, we propose three path models to address the obstacle avoidance problem for AMRs. To evaluate the performance of our energy prediction and path models, we have developed a customized AMR called Donkey, which has the capability for fine-grained (millisecond-level) end-to-end power profiling. Our energy prediction model demonstrated an accuracy of over 90% in our evaluations. Finally, we applied our energy prediction model to obstacle avoidance and guided energy-efficient path selection, resulting in up to a 44.8% reduction in energy consumption compared to the baseline. 

Mobile computing devices have been used to store and process sensitive or even mission critical data. To protect sensitive data in mobile devices, encryption is usually incorporated into major mobile operating systems. However, traditional encryption can not defend against coercive attacks in which victims are forced to disclose the key used to decrypt the sensitive data. To combat the coercive attackers, plausibly deniable encryption (PDE) has been introduced which can allow the victims to deny the existence of the sensitive data. However, the existing PDE systems designed for mobile devices are either insecure (i.e., suffering from deniability compromises) or impractical (i.e., unable to be compatible with the storage architecture of mainstream mobile devices, not lightweight, or not user-oriented). In this work, we design CrossPDE, the first cross-layer mobile PDE system which is secure, being compatible with the storage architecture of mainstream mobile devices, lightweight as well as user-oriented. Our key idea is to intercept major layers of a mobile storage system, including the file system layer (preventing loss of hidden sensitive data and enabling users to use the hidden mode), the block layer (taking care of expensive encryption and decryption), and the flash translation layer (eliminating traces caused by the hidden sensitive data). Experimental evaluation on our real-world prototype shows that CrossPDE can ensure deniability with a modest decrease in throughput. 

Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.