Digital library lending is a critical resource for access to information. Currently prevalent models of digital lending, however, involve opaque licensing schemes that entail serious drawbacks to reader privacy and freedom of expression. In popular modern library apps, publishers and hidden intermediaries control a wealth of information about readers and reading habits, at a scale and level of detail that would be essentially impossible in physical library lending. To understand digital lending needs in practice, our work begins with a series of interviews with library professionals (N=11). We present thematic findings on their concerns with existing systems, including privacy, surveillance, preservation, and lack of library control over resources. Many of the concerns raised are inherently unproblematic in the context of physical library lending---leading us to our central technical question: Can digital lending achieve privacy and transparency at least as strong as physical library lending? Based on our qualitative findings, we provide the first rigorous modeling of security, privacy, and transparency requirements in digital library lending. As existing systems fall short of the strong guarantees we model, we propose a new system design, LendLocked, based on cryptography and trusted hardware, and prove it achieves these guarantees in the random oracle model. We micro-benchmark our design's key cryptographic functionalities, showing tolerable efficiency at the scale of the largest libraries.

With the frequent use of self-supervised monocular depth estimation in robotics and autonomous driving, the model’s efficiency is becoming increasingly important. Most current approaches apply much larger and more complex networks to improve the precision of depth estimation. Some researchers incorporated Transformer into self-supervised monocular depth estimation to achieve better performance. However, this method leads to high parameters and high computation. We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects instead of long-range fusion. We further promote depth estimation results employing lightweight channel attention based on convolution in the decoder stage. Our method reduces the parameters without sacrificing accuracy. Experiments on the KITTI benchmark show that our method can get better results than many large models, such as Monodepth2, with only 30% parameters. The source code is available at https://github.com/boyagesmile/DNA-Depth.

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at the cost of collecting large amount of personal information. This lack of privacy can endanger recipients’ safety and harm their dignity. In collaboration with the International Committee of the Red Cross, we build a safe digital aid-distribution system. We first systematize the requirements such a system should satisfy. We then propose a decentralized solution based on the use of tokens that fulfills the needs of humanitarian organizations. It provides scalability and strong accountability, and, by design, guarantees the recipients’ privacy. We provide two instantiations of our design, on a smart card and on a smartphone. We formally prove the security and privacy properties of these solutions, and empirically show that they can operate at scale.

We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.

In the past decade, the vulnerability of aircraft communications against low-resourced attackers has received significant attention both in the information security community and from aviation industry and regulators. Until now, research on attacks against such communications technologies has focused on larger aircraft, neglecting the technologies used in light aircraft and unmanned aerial vehicles (UAV). As such lighter aircraft make up a large and growing majority of both airspace users and casualties, this is a glaring oversight from a security and safety perspective.