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RISS 인기검색어
- 검색키워드
- 저자 : ( Laifeng Lu ) (검색결과 4 건)
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Mingguan Yang,Laifeng Lu,Jing Pang,Yiling Hu,Qingbin Guo,Zhenjing Li,Shufen Wu,Huanhuan Liu,Changlu Wang 한국미생물학회 2019 The journal of microbiology Vol.57 No.5
Aspergillus flavus is a saprophytic fungus that contaminates crops with carcinogenic aflatoxin. In the present work, the antifungal effects of volatile organic compounds (VOCs) from Streptomyces alboflavus TD-1 against A. flavus were investigated. VOCs from 8-day-old wheat bran culture of S. alboflavus TD-1 displayed strong inhibitory effects against mycelial growth, sporulation, and conidial germination of A. flavus. Severely misshapen conidia and hyphae of A. flavus were observed by scanning electron microscopy after exposure to VOCs for 6 and 12 h, respectively. Rhodamine 123 staining of mitochondria indicated that mitochondria may be a legitimate antifungal target of the VOCs from S. alboflavus TD-1. Furthermore, the VOCs effectively inhibited aflatoxin B1 production by downregulating genes involved in aflatoxin biosynthesis. Dimethyl trisulfide and benzenamine may play important roles in the suppression of A. flavus growth and production of aflatoxin. The results indicate that VOCs from S. alboflavus TD-1 have tremendous potential to be developed as a useful bio-pesticide for controlling A. flavus.
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A Differential Privacy Approach to Preserve GWAS Data Sharing based on A Game Theoretic Perspective
Jun Yan,Ziwei Han,Yihui Zhou,Laifeng Lu 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.3
Genome-wide association studies (GWAS) aim to find the significant genetic variants for common complex disease. However, genotype data has privacy information such as disease status and identity, which make data sharing and research difficult. Differential privacy is widely used in the privacy protection of data sharing. The current differential privacy approach in GWAS pays no attention to raw data but to statistical data, and doesn’t achieve equilibrium between utility and privacy, so that data sharing is hindered and it hampers the development of genomics. To share data more securely, we propose a differential privacy preserving approach of data sharing for GWAS, and achieve the equilibrium between privacy and data utility. Firstly, a reasonable disturbance interval for the genotype is calculated based on the expected utility. Secondly, based on the interval, we get the Nash equilibrium point between utility and privacy. Finally, based on the equilibrium point, the original genotype matrix is perturbed with differential privacy, and the corresponding random genotype matrix is obtained. We theoretically and experimentally show that the method satisfies expected privacy protection and utility. This method provides engineering guidance for protecting GWAS data privacy.
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Adaptive Gaussian Mechanism Based on Expected Data Utility under Conditional Filtering Noise
( Hai Liu ),( Zhenqiang Wu ),( Changgen Peng ),( Feng Tian ),( Laifeng Lu ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.7
Differential privacy has broadly applied to statistical analysis, and its mainly objective is to ensure the tradeoff between the utility of noise data and the privacy preserving of individual’s sensitive information. However, an individual could not achieve expected data utility under differential privacy mechanisms, since the adding noise is random. To this end, we proposed an adaptive Gaussian mechanism based on expected data utility under conditional filtering noise. Firstly, this paper made conditional filtering for Gaussian mechanism noise. Secondly, we defined the expected data utility according to the absolute value of relative error. Finally, we presented an adaptive Gaussian mechanism by combining expected data utility with conditional filtering noise. Through comparative analysis, the adaptive Gaussian mechanism satisfies differential privacy and achieves expected data utility for giving any privacy budget. Furthermore, our scheme is easy extend to engineering implementation.
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An Uncertain Graph Method Based on Node Random Response to Preserve Link Privacy of Social Networks
Jun Yan,Jiawang Chen,Yihui Zhou,Zhenqiang Wu,Laifeng Lu 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.1
In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users’ sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.
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