450 research outputs found
Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency
Recently, several practical attacks raised serious concerns over the security
of searchable encryption. The attacks have brought emphasis on forward privacy,
which is the key concept behind solutions to the adaptive leakage-exploiting
attacks, and will very likely to become mandatory in the design of new
searchable encryption schemes. For a long time, forward privacy implies
inefficiency and thus most existing searchable encryption schemes do not
support it. Very recently, Bost (CCS 2016) showed that forward privacy can be
obtained without inducing a large communication overhead. However, Bost's
scheme is constructed with a relatively inefficient public key cryptographic
primitive, and has a poor I/O performance. Both of the deficiencies
significantly hinder the practical efficiency of the scheme, and prevent it
from scaling to large data settings. To address the problems, we first present
FAST, which achieves forward privacy and the same communication efficiency as
Bost's scheme, but uses only symmetric cryptographic primitives. We then
present FASTIO, which retains all good properties of FAST, and further improves
I/O efficiency. We implemented the two schemes and compared their performance
with Bost's scheme. The experiment results show that both our schemes are
highly efficient, and FASTIO achieves a much better scalability due to its
optimized I/O
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem
The 0-1 multidimensional knapsack problem(MKP) is a classical NP-hard
combinatorial optimization problem. In this paper, we propose a novel heuristic
algorithm simulating evolutionary computation and large neighbourhood search
for the MKP. It maintains a set of solutions and abstracts information from the
solution set to generate good partial assignments. To find high-quality
solutions, integer programming is employed to explore the promising search
space specified by the good partial assignments. Extensive experimentation with
commonly used benchmark sets shows that our approach outperforms the state of
the art heuristic algorithms, TPTEA and DQPSO, in solution quality. It finds
new lower bound for 8 large and hard instance
Construction of Hydrogen Safety Evaluation Model Based on Analytic Hierarchy Process (AHP)
With the large consumption of traditional primary energy, hydrogen as a clean and renewable energy has been widely studied by scholars around the world. Hydrogen is mainly used in hydrogen internal combustion engine and hydrogen fuel cell. Hydrogen internal combustion engine is the direct combustion of hydrogen as fuel, with the advantages of easy use. Alternatively, hydrogen fuel cell converts the chemical energy of hydrogen into electrical energy by electrochemical reaction, which has the advantages of high efficiency and zero pollution. Regardless of the use method, the safety of hydrogen use needs to be considered. However, in the whole life cycle of hydrogen, the process from hydrogen production to the use of hydrogen in automobiles is extremely complex. There are many factors affecting the safety of hydrogen use, and a single factor cannot be used as an evaluation. In order to make the evaluation of hydrogen safety more complete and accurate, the weight of four primary evaluation indexes and eight secondary evaluation indexes affecting hydrogen safety is determined by analytic hierarchy process, and a reliable hydrogen safety evaluation model is established.Citation:Â Xu, J., Wang, M., and Guo, P. (2022). Construction of Hydrogen Safety Evaluation Model Based on Analytic Hierarchy Process (AHP). Trends in Renewable Energy, 8(2), 84-95. DOI: http://dx.doi.org/10.17737/tre.2022.8.2.0014
Improving spam filtering in enterprise email systems with blockchain-based token incentive mechanism
Spam has caused serious problems for email systems. To address this issue, numerous spam filter algorithms have been developed, all of which require extensive training on labeled spam datasets to obtain the desired filter performance. However, users\u27 privacy concerns and apathy make it difficult to acquire personalized spam data in real-world applications. When it comes to enterprise email systems, the problem worsens because enterprises are extremely sensitive to the possible disclosure of confidential information during the reporting of spam to the cloud. Targeting these obstacles, this study proposes a blockchain-based token incentive mechanism, with the aim of encouraging users to report spam while protecting business secrets and ensuring the transparency of reward rules. The proposed mechanism also enables a decentralized ecosystem for token circulation, fully utilizing the advantages of blockchain technologies. We developed a prototype of the proposed system, on which we conducted a user experiment to verify our design. Results indicate that the proposed incentive mechanism is effective and can raise the probability of spam reporting by more than 1.4 times
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