Practical Volume-Based Attacks on Encrypted Databases

Recent years have seen an increased interest towards strong security primitives for encrypted databases (such as oblivious protocols), that hide the access patterns of query execution, and reveal only the volume of results. However, recent work has shown that even volume leakage can enable the reconstruction of entire columns in the database. Yet, existing attacks rely on a set of assumptions that are unrealistic in practice: for example, they (i) require a large number of queries to be issued by the user, or (ii) assume certain distributions on the queries or underlying data (e.g., that the queries are distributed uniformly at random, or that the database does not contain missing values). In this work, we present new attacks for recovering the content of individual user queries, assuming no leakage from the system except the number of results and avoiding the limiting assumptions above. Unlike prior attacks, our attacks require only a single query to be issued by the user for recovering the keyword. Furthermore, our attacks make no assumptions about the distribution of issued queries or the underlying data. Instead, our key insight is to exploit the behavior of real-world applications. We start by surveying 11 applications to identify two key characteristics that can be exploited by attackers: (i) file injection, and (ii) automatic query replay. We present attacks that leverage these two properties in concert with volume leakage, independent of the details of any encrypted database system. Subsequently, we perform an attack on the real Gmail web client by simulating a server-side adversary. Our attack on Gmail completes within a matter of minutes, demonstrating the feasibility of our techniques. We also present three ancillary attacks for situations when certain mitigation strategies are employed.Comment: IEEE EuroS&P 202

Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning

Traffic flow prediction is an important part of smart transportation. The goal is to predict future traffic conditions based on historical data recorded by sensors and the traffic network. As the city continues to build, parts of the transportation network will be added or modified. How to accurately predict expanding and evolving long-term streaming networks is of great significance. To this end, we propose a new simulation-based criterion that considers teaching autonomous agents to mimic sensor patterns, planning their next visit based on the sensor's profile (e.g., traffic, speed, occupancy). The data recorded by the sensor is most accurate when the agent can perfectly simulate the sensor's activity pattern. We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network. Actions taken by the agent change the environment, which in turn forces the agent's mode to update, while the agent further explores changes in the dynamic traffic network, which helps the agent predict its next visit more accurately. Therefore, we develop a strategy in which sensors and traffic networks update each other and incorporate temporal context to quantify state representations evolving over time

Dual Semantic Fusion Network for Video Object Detection

Video object detection is a tough task due to the deteriorated quality of video sequences captured under complex environments. Currently, this area is dominated by a series of feature enhancement based methods, which distill beneficial semantic information from multiple frames and generate enhanced features through fusing the distilled information. However, the distillation and fusion operations are usually performed at either frame level or instance level with external guidance using additional information, such as optical flow and feature memory. In this work, we propose a dual semantic fusion network (abbreviated as DSFNet) to fully exploit both frame-level and instance-level semantics in a unified fusion framework without external guidance. Moreover, we introduce a geometric similarity measure into the fusion process to alleviate the influence of information distortion caused by noise. As a result, the proposed DSFNet can generate more robust features through the multi-granularity fusion and avoid being affected by the instability of external guidance. To evaluate the proposed DSFNet, we conduct extensive experiments on the ImageNet VID dataset. Notably, the proposed dual semantic fusion network achieves, to the best of our knowledge, the best performance of 84.1\% mAP among the current state-of-the-art video object detectors with ResNet-101 and 85.4\% mAP with ResNeXt-101 without using any post-processing steps.Comment: 9 pages,6 figure

DYNLT3 overexpression induces apoptosis and inhibits cell growth and migration via inhibition of the Wnt pathway and EMT in cervical cancer

The role of the dynein light chain Tctex-type 3 (DYNLT3) protein in the biological behavior of cervical cancer and its relative molecular mechanisms were investigated. Immunohistochemical staining was used to detect DYNLT3 protein expression in cervical cancer tissues. Cell proliferation and apoptosis rates and invasiveness and migratory capacities were determined by CCK-8 assays, BrdU staining assays and colony formation assays, fluorescence activated cell sorting (FACS), wound healing assays, and Transwell invasion assays of cervical cancer cells after DYNLT3 modulation. The expression levels of Wnt signaling pathway- and EMT-related proteins were examined by Western blotting. Furthermore, the effects of DYNLT3 on the tumorigenicity and metastasis of cervical cancer in nude mice were analyzed by performing immunohistochemistry, and we found that the expression level of the DYNLT3 protein was higher in human normal cervical tissues than in cervical cancer tissues. Overexpression of DYNLT3 obviously attenuated the proliferation, migration and invasion of CaSki and SiHa cells, and promoted cell apoptosis. Upregulation of DYNLT3 expression markedly decreased the expression of Wnt signaling pathway-related proteins (Dvl2, Dvl3, p-LRP6, Wnt3a, Wnt5a/b, Naked1, Naked2, β-catenin and C-Myc) and EMT-related proteins (N-cadherin, SOX2, OCT4, vimentin and Snail), and increased the expression of E-cadherin and Axin1. However, the opposite results were observed after down-regulation of DYNLT3 expression. Up-regulation of DYNLT3 expression significantly inhibited tumor growth in a nude mouse model, while downregulation of DYNLT3 showed the opposite results. In addition, the major metastatic site of cervical cancer cells in mice was the lung, and downregulation of DYNLT3 expression increased cancer metastasis in vivo. DYNLT3 exerted inhibitory effects on cervical cancer by inhibiting cell proliferation, migration and invasion, promoting cell apoptosis in vitro, and inhibiting tumor growth and metastasis in vivo, possibly by suppressing the Wnt signaling pathway and the EMT