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RISS 인기검색어
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- 저자 : ( Zehong Chen ) (검색결과 3 건)
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Ciphertext policy attribute-based encryption supporting unbounded attribute space from R-LWE
( Zehong Chen ),( Peng Zhang ),( Fangguo Zhang ),( Jiwu Huang ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.4
Ciphertext policy attribute-based encryption (CP-ABE) is a useful cryptographic technology for guaranteeing data confidentiality but also fine-grained access control. Typically, CP-ABE can be divided into two classes: small universe with polynomial attribute space and large universe with unbounded attribute space. Since the learning with errors over rings (R-LWE) assumption has characteristics of simple algebraic structure and simple calculations, based on R-LWE, we propose a small universe CP-ABE scheme to improve the efficiency of the scheme proposed by Zhang et al. (AsiaCCS 2012). On this basis, to achieve unbounded attribute space and improve the expression of attribute, we propose a large universe CP-ABE scheme with the help of a full-rank differences function. In this scheme, all polynomials in the R-LWE can be used as values of an attribute, and these values do not need to be enumerated at the setup phase. Different trapdoors are used to generate secret keys in the key generation and the security proof. Both proposed schemes are selectively secure in the standard model under R-LWE. Comparison with other schemes demonstrates that our schemes are simpler and more efficient. R-LWE can obtain greater efficiency, and unbounded attribute space means more flexibility, so our research is suitable in practices.
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Filter Contribution Recycle: Boosting Model Pruning with Small Norm Filters
Zehong Chen,Zhonghua Xie,Zhen Wang,Tao Xu,Zhengrui Zhang 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.11
Model pruning methods have attracted huge attention owing to the increasing demand of deploying models on low-resource devices recently. Most existing methods use the weight norm of filters to represent their importance, and discard the ones with small value directly to achieve the pruning target, which ignores the contribution of the small norm filters. This is not only results in filter contribution waste, but also gives comparable performance to training with the random initialized weights [1]. In this paper, we point out that the small norm filters can harm the performance of the pruned model greatly, if they are discarded directly. Therefore, we propose a novel filter contribution recycle (FCR) method for structured model pruning to resolve the fore-mentioned problem. FCR collects and reassembles contribution from the small norm filters to obtain a mixed contribution collector, and then assigns the reassembled contribution to other filters with higher probability to be preserved. To achieve the target FLOPs, FCR also adopts a weight decay strategy for the small norm filters. To explore the effectiveness of our approach, extensive experiments are conducted on ImageNet2012 and CIFAR-10 datasets, and superior results are reported when comparing with other methods under the same or even more FLOPs reduction. In addition, our method is flexible to be combined with other different pruning criterions.
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Ginsenosides Rc, as a novel SIRT6 activator, protects mice against high fat diet induced NAFLD
Zehong Yang,Yuanyuan Yu,Nannan Sun,Limian Zhou,Dong Zhang,HaiXin Chen,Wei Miao,Weihang Gao,Canyang Zhang,Changhui Liu,Xiaoying Yang,Xiaojie Wu,Yong Gao The Korean Society of Ginseng 2023 Journal of Ginseng Research Vol.47 No.3
Background: Hepatic lipid disorder impaired mitochondrial homeostasis and intracellular redox balance, triggering development of non-alcohol fatty liver disease (NAFLD), while effective therapeutic approach remains inadequate. Ginsenosides Rc has been reported to maintain glucose balance in adipose tissue, while its role in regulating lipid metabolism remain vacant. Thus, we investigated the function and mechanism of ginsenosides Rc in defending high fat diet (HFD)-induced NAFLD. Methods: Mice primary hepatocytes (MPHs) challenged with oleic acid & palmitic acid were used to test the effects of ginsenosides Rc on intracellular lipid metabolism. RNAseq and molecular docking study were performed to explore potential targets of ginsenosides Rc in defending lipid deposition. Wild type and liver specific sirtuin 6 (SIRT6, 50721) deficient mice on HFD for 12 weeks were subjected to different dose of ginsenosides Rc to determine the function and detailed mechanism in vivo. Results: We identified ginsenosides Rc as a novel SIRT6 activator via increasing its expression and deacetylase activity. Ginsenosides Rc defends OA&PA-induced lipid deposition in MPHs and protects mice against HFD-induced metabolic disorder in dosage dependent manner. Ginsenosides Rc (20mg/kg) injection improved glucose intolerance, insulin resistance, oxidative stress and inflammation response in HFD mice. Ginsenosides Rc treatment accelerates peroxisome proliferator activated receptor alpha (PPAR-α, 19013)-mediated fatty acid oxidation in vivo and in vitro. Hepatic specific SIRT6 deletion abolished ginsenoside Rc-derived protective effects against HFD-induced NAFLD. Conclusion: Ginsenosides Rc protects mice against HFD-induced hepatosteatosis by improving PPAR-α-mediated fatty acid oxidation and antioxidant capacity in a SIRT6 dependent manner, and providing a promising strategy for NAFLD.
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