File-Injection Attacks on Searchable Encryption, Based on Binomial Structures

One distinguishable feature of file-inject attacks on searchable encryption schemes is the 100% query recovery rate, i.e., confirming the corresponding keyword for each query. The main efficiency consideration of file-injection attacks is the number of injected files. In the work of Zhang et al. (USENIX 2016), $|\log_2|K||$ injected files are required, each of which contains $|K|/2$ keywords for the keyword set $K$. Based on the construction of the uniform $(s,n)$-set, Wang et al. need fewer injected files when considering the threshold countermeasure. In this work, we propose a new attack that further reduces the number of injected files where Wang et al. need up to 38% more injections to achieve the same results. The attack is based on an increment $(s,n)$-set, which is also defined in this paper

CCA-1 Secure Updatable Encryption with Adaptive Security

Updatable encryption (UE) enables a cloud server to update ciphertexts using client-generated tokens. There are two types of UE: ciphertext-independent (c-i) and ciphertext-dependent (c-d). In terms of construction and efficiency, c-i UE utilizes a single token to update all ciphertexts. The update mechanism relies mainly on the homomorphic properties of exponentiation, which limits the efficiency of encryption and updating. Although c-d UE may seem inconvenient as it requires downloading parts of the ciphertexts during token generation, it allows for easy implementation of the Dec-then-Enc structure. This methodology significantly simplifies the construction of the update mechanism. Notably, the c-d UE scheme proposed by Boneh et al. (ASIACRYPT’20) has been reported to be 200 times faster than prior UE schemes based on DDH hardness, which is the case for most existing c-i UE schemes. Furthermore, c-d UE ensures a high level of security as the token does not reveal any information about the key, which is difficult for c-i UE to achieve. However, previous security studies on c-d UE only addressed selective security; the studies for adaptive security remain an open problem. In this study, we make three significant contributions to ciphertextdependent updatable encryption (c-d UE). Firstly, we provide stronger security notions compared to previous work, which capture adaptive security and also consider the adversary’s decryption capabilities under the adaptive corruption setting. Secondly, we propose a new c-d UE scheme that achieves the proposed security notions. The token generation technique significantly differs from the previous Dec-then-Enc structure, while still preventing key leakages. At last, we introduce a packing technique that enables the simultaneous encryption and updating of multiple messages within a single ciphertext. This technique helps alleviate the cost of c-d UE by reducing the need to download partial ciphertexts during token generation

Dynamic Simulations on the Arachidonic Acid Metabolic Network

Drug molecules not only interact with specific targets, but also alter the state and function of the associated biological network. How to design drugs and evaluate their functions at the systems level becomes a key issue in highly efficient and low–side-effect drug design. The arachidonic acid metabolic network is the network that produces inflammatory mediators, in which several enzymes, including cyclooxygenase-2 (COX-2), have been used as targets for anti-inflammatory drugs. However, neither the century-old nonsteriodal anti-inflammatory drugs nor the recently revocatory Vioxx have provided completely successful anti-inflammatory treatment. To gain more insights into the anti-inflammatory drug design, the authors have studied the dynamic properties of arachidonic acid (AA) metabolic network in human polymorphous leukocytes. Metabolic flux, exogenous AA effects, and drug efficacy have been analyzed using ordinary differential equations. The flux balance in the AA network was found to be important for efficient and safe drug design. When only the 5-lipoxygenase (5-LOX) inhibitor was used, the flux of the COX-2 pathway was increased significantly, showing that a single functional inhibitor cannot effectively control the production of inflammatory mediators. When both COX-2 and 5-LOX were blocked, the production of inflammatory mediators could be completely shut off. The authors have also investigated the differences between a dual-functional COX-2 and 5-LOX inhibitor and a mixture of these two types of inhibitors. Their work provides an example for the integration of systems biology and drug discovery

Inject Less, Recover More: Unlocking the Potential of Document Recovery in Injection Attacks Against SSE

Searchable symmetric encryption has been vulnerable to inference attacks that rely on uniqueness in leakage patterns. However, many keywords in datasets lack distinctive leakage patterns, limiting the effectiveness of such attacks. The file injection attacks, initially proposed by Cash et al. (CCS 2015), have shown impressive performance with 100% accuracy and no prior knowledge requirement. Nevertheless, this attack fails to recover queries with underlying keywords not present in the injected files. To address these limitations, our research introduces a novel attack strategy called LEAP-Hierarchical Fusion Attack (LHFA) that combines the strengths of both file injection attacks and inference attacks. Before initiating keyword injection, we introduce a new approach for inert/active keyword selection. In the phase of selecting injected keywords, we focus on keywords without unique leakage patterns and recover them, leveraging their presence for document recovery. Our goal is to achieve an amplified effect in query recovery. We demonstrate a minimum query recovery rate of 1.3 queries per injected keyword with a 10% data leakage of a real-life dataset, and initiate further research to overcome challenges associated with non-distinctive keywords

Similar Data is Powerful: Enhancing Inference Attacks on SSE with Volume Leakages

Searchable symmetric encryption (SSE) schemes provide users with the ability to perform keyword searches on encrypted databases without the need for decryption. While this functionality is advantageous, it introduces the potential for inadvertent information disclosure, thereby exposing SSE systems to various types of attacks. In this work, we introduce a new inference attack aimed at enhancing the query recovery accuracy of RefScore (presented at USENIX 2021). The proposed approach capitalizes on both similar data knowledge and an additional volume leakage as auxiliary information, facilitating the extraction of keyword matches from leaked data. Empirical evaluations conducted on multiple real-world datasets demonstrate a notable enhancement in query recovery accuracy, up to 19.5%. We also analyze the performance of the proposed attack in the presence of diverse countermeasures

Volume and Access Pattern Leakage-abuse Attack with Leaked Documents

Searchable Encryption schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access and volume pattern. In most existing SE schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Recent attacks have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Under the umbrella of attacking SE, we design a new Volume and Access Pattern Leakage-Abuse Attack (VAL-Attack) that improves the matching technique of LEAP (CCS ’21) and exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs without false positives. We further compare VAL-Attack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures

Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer

BackgroundThe poor clinical accuracy to predict the survival of colon cancer patients is associated with a high incidence rate and a poor 3-year survival rate. This study aimed to identify the poor prognostic biomarkers of colon cancer from natural killer cell-mediated cytotoxic pathway (NKCP), and establish a logistical regression scoring model to predict its prognosis.MethodsBased on the expressions and methylations of NKCP-related genes (NRGs) and the clinical information, dimensionality reduction screening was performed to establish a logistic regression scoring model to predict survival and prognosis. Risk score, clinical stage, and ULBP2 were used to establish a logistic regression scoring model to classify the 3-year survival period and compare with each other. Comparison of survival, tumor mutation burden (TMB), estimation of immune invasion, and prediction of chemotherapeutic drug IC50 were performed between low- and high-risk score groups.ResultsThis study found that ULBP2 was significantly overexpressed in colon cancer tissues and colon cancer cell lines. The logistic regression scoring model was established to include six statistically significant features: S = 1.70 × stage – 9.32 × cg06543087 + 6.19 × cg25848557 + 1.29 × IFNA1 + 0.048 × age + 4.37 × cg21370856 − 8.93, which was used to calculate risk score of each sample. The risk scores, clinical stage, and ULBP2 were classified into three-year survival, the 3-year prediction accuracy based on 10-fold cross-validation was 80.17%, 67.24, and 59.48%, respectively. The survival time of low-risk score group was better than that of the high-risk score group. Moreover, compared to high-risk score group, low-risk score group had lower TMB [2.20/MB (log10) vs. 2.34/MB (log10)], higher infiltration score of M0 macrophages (0.17 vs. 0.14), and lower mean IC50 value of oxaliplatin (3.65 vs 3.78) (p < 0.05).ConclusionsThe significantly upregulated ULBP2 was a poor prognostic biomarker of colon cancer. The risk score based on the six-feature logistic regression model can effectively predict the 3-year survival time. High-risk score group demonstrated a poorer prognosis, higher TMB, lower M0 macrophage infiltration score, and higher IC50 value of oxaliplatin. The six-feature logistic scoring model has certain clinical significance in colon cancer

Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSP instances demonstrate that NAR4TSP outperforms four state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios.Comment: 14 pages, 5 figure

Comparisons of three polyethyleneimine-derived nanoparticles as a gene therapy delivery system for renal cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Polyethyleneimine (PEI), which can interact with negatively charged DNA through electrostatic interaction to form nanocomplexes, has been widely attempted to use as a gene delivery system. However, PEI has some defects that are not fit for keeping on gene expression. Therefore, some modifications against PEI properties have been done to improve their application value in gene delivery. In this study, three modified PEI derivatives, including poly(ε-caprolactone)-pluronic-poly(ε-caprolactone) grafted PEI (PCFC-g-PEI), folic acid-PCFC-isophorone diidocyanate-PEI (FA-PEAs) and heparin-PEI (HPEI), were evaluated in terms of their cytotoxicity and transfection efficiency <it>in vitro </it>and <it>in vivo </it>in order to ascertain their potential application in gene therapy.</p> <p>Methods</p> <p>MTT assay and a marker GFP gene, encoding green fluorescent protein, were used to evaluate cell toxicity and transfection activity of the three modified PEI <it>in vitro</it>. Renal cell carcinoma (RCC) models were established in BALB/c nude mice inoculated with OS-RC-2 cells to detect the gene therapy effects using the three PEI-derived nanoparticles as gene delivery vehicles. The expression status of a target gene Von Hippel-Lindau (VHL) in treated tumor tissues was analyzed by semiquantitative RT-PCR and immunohistochemistry.</p> <p>Results</p> <p>Each of three modified PEI-derived biomaterials had an increased transfection efficiency and a lower cytotoxicity compared with its precursor PEI with 25-kD or 2-kD molecule weight <it>in vitro</it>. And the mean tumor volume was obviously decreased 30% by using FA-PEAs to transfer VHL plasmids to treat mice RCC models. The VHL gene expression was greatly improved in the VHL-treated group. While there was no obvious tumor inhibition treated by PCFC-g-PEI:VHL and HPEI:VHL complexes.</p> <p>Conclusions</p> <p>The three modified PEI-derived biomaterials, including PCFC-g-PEI, FA-PEAs and HPEI, had an increased transfection efficiency <it>in vitro </it>and obviously lower toxicities compared with their precursor PEI molecules. The FA-PEAs probably provide a potential gene delivery system to treat RCC even other cancers in future.</p

Trade-induced atmospheric mercury deposition over China and implications for demand-side controls

Mercury (Hg) is of global concern because of its adverse effects on humans and the environment. In addition to long-range atmospheric transport, Hg emissions can be geographically relocated through economic trade. Here, we investigate the effect of China’s interregional trade on atmospheric Hg deposition over China, using an atmospheric transport model and multiregional input-output analysis. In general, total atmospheric Hg deposition over China is 408.8 Mg yr-1, and 32% of this is embodied in China’s interregional trade, with the hotspots occurring over Gansu, Henan, Hebei, and Yunnan provinces. Interprovincial trade considerably redistributes atmospheric Hg deposition over China, with a range in deposition flux from −104% to +28%. Developed regions, such as the Yangtze River Delta (Shanghai, Jiangsu, and Zhejiang) and Guangdong, avoid Hg deposition over their geographical boundaries, instead causing additional Hg deposition over developing provinces. Bilateral interaction among provinces is strong over some regions, suggesting a need for joint mitigation, such as the Jing-Jin-Ji region (Beijing, Tianjin, and Hebei) and the Yangtze River Delta. Transferring advanced technology from developed regions to their developing trade partners would be an effective measure to mitigate China’s Hg pollution. Our findings are relevant to interprovincial efforts to reduce trans-boundary Hg pollution in China