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While our society accelerates its transition to the Internet of Things, billions of IoT devices are now linked to the network. While these gadgets provide enormous convenience, they generate a large amount of data that has already beyond the network’s capacity. To make matters worse, the data acquired by sensors on such IoT devices also include sensitive user data that must be appropriately treated. At the moment, the answer is to provide hub services for data storage in data centers. However, when data is housed in a centralized data center, data owners lose control of the data, since data centers are centralized solutions that rely on data owners’ faith in the service provider. In addition, edge computing enables edge devices to collect, analyze, and act closer to the data source, the challenge of data privacy near the edge is also a tough nut to crack. A large number of user information leakage both for IoT hub and edge made the system untrusted all along. Accordingly, building a decentralized IoT system near the edge and bringing real trust to the edge is indispensable and significant. To eliminate the need for a centralized data hub, we present a prototype of a unique,more »secure, and decentralized IoT framework called Reja, which is built on a permissioned Blockchain and an intrusion-tolerant messaging system ChiosEdge, and the critical components of ChiosEdge are reliable broadcast and BFT consensus. We evaluated the latency and throughput of Reja and its sub-module ChiosEdge.« less

Modern mobile devices are increasingly used to store and process sensitive data. In order to prevent the sensitive data from being leaked, one of the best ways of protecting them and their owner is to hide the data with plausible deniability. Plausibly Deniable Encryption (PDE) has been designed for such purpose. The existing PDE systems for mobile devices however, have suffered from significant drawbacks as they either ignore the deniability compromises present in the special underlying storage media of mobile devices or are vulnerable to various new attacks such as side-channel attacks. In this work, we propose a new PDE system design for mobile devices which takes advantage of the hardware features equipped in the mainstream mobile devices. Our preliminary design has two major component: First, we strictly isolate the hidden and the public data in the flash layer, so that a multi-snapshot adversary is not able to identify the existence of the hidden sensitive data when having access to the low layer storage medium of the device. Second, we incorporate software and operating system level deniability into ARM TrustZone. With this TrustZone-enhanced isolation, our PDE system is immune to side-channel attacks at the operating system layer.

Mobile computing devices are widely used in our daily life. With their increased use, a large amount of sensitive data are collected, stored, and managed in the mobile devices. To protect sensitive data, encryption is often used but, traditional encryption is vulnerable to coercive attacks in which the device owner is coerced by the adversary to disclose the decryption key. To defend against the coercive attacks, 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 mobile devices are still missing in the literature. In this work, we design MobiWear, the first PDE system specifically for wearable mobile devices. To accommodate the hardware nature of wearable devices, MobiWear: 1) uses image steganography to achieve PDE, which suits the resource-limited wearable devices; and 2) relies on various sensors equipped with the wearable devices to input passwords, rather than requiring users to enter them via a keyboard or a touchscreen. Security analysis and experimental evaluation using a real-world prototype (ported to an LG G smartwatch) show that MobiWear can ensure deniability with amore »small computational overhead as well as a small decrease of image quality.« less

Autonomous mobile robots (AMRs) have been widely utilized in industry to execute various on-board computer-vision applications including autonomous guidance, security patrol, object detection, and face recognition. Most of the applications executed by an AMR involve the analysis of camera images through trained machine learning models. Many research studies on machine learning focus either on performance without considering energy efficiency or on techniques such as pruning and compression to make the model more energy-efficient. However, most previous work do not study the root causes of energy inefficiency for the execution of those applications on AMRs. The computing stack on an AMR accounts for 33% of the total energy consumption and can thus highly impact the battery life of the robot. Because recharging an AMR may disrupt the application execution, it is important to efficiently utilize the available energy for maximized battery life. In this paper, we first analyze the breakdown of power dissipation for the execution of computer-vision applications on AMRs and discover three main root causes of energy inefficiency: uncoordinated access to sensor data, performance-oriented model inference execution, and uncoordinated execution of concurrent jobs. In order to fix these three inefficiencies, we propose E2M, an energy-efficient middleware software stack formore »autonomous mobile robots. First, E2M regulates the access of different processes to sensor data, e.g., camera frames, so that the amount of data actually captured by concurrently executing jobs can be minimized. Second, based on a predefined per-process performance metric (e.g., safety, accuracy) and desired target, E2M manipulates the process execution period to find the best energy-performance trade off. Third, E2M coordinates the execution of the concurrent processes to maximize the total contiguous sleep time of the computing hardware for maximized energy savings. We have implemented a prototype of E2M on a real-world AMR. Our experimental results show that, compared to several baselines, E2M leads to 24% energy savings for the computing platform, which translates into an extra 11.5% of battery time and 14 extra minutes of robot runtime, with a performance degradation lower than 7.9% for safety and 1.84% for accuracy.« less