Xi Chen

Work place: Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong, Hong Kong, China

E-mail: dlkcissy@hku.hk


Research Interests: Computational Science and Engineering, Autonomic Computing, Solid Modeling, Data Structures and Algorithms, Mathematics of Computing


Xi Chen got her B. Sc. in applied mathematics from Dalian University of Technology. Currently she is a Ph.D student and her research interest is mathematical modeling, scientific computing and bioinformatics.

Author Articles
Efficient Proxy Re-encryption with Private Searching in the Untrusted Cloud

By Xi Chen Yong Li

DOI: https://doi.org/10.5815/ijwmt.2012.01.08, Pub. Date: 15 Feb. 2012

As promising as cloud computing is, this paradigm brings forth new security and privacy challenges when operating in the untrusted cloud scenarios. In this paper, we propose a new cryptographic primitive Proxy Re-encryption with Private Searching (PRPS for short). The PRPS scheme enables the data users and owners efficiently query and access files storaged in untrusted cloud, while keeping query privacy and data privacy from the cloud providers. The concrete construction is based on proxy re-encryption, public key encryption with keyword search and the dual receiver cryptosystem. The scheme is semantically secure under the BDH assumption.

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On Construction of Gene-PDB Structure Mapping with Applications in Functional Annotation of Human Genes

By Xi Chen Hao Jiang Wai-Ki Ching Limin Li

DOI: https://doi.org/10.5815/ijitcs.2011.02.08, Pub. Date: 8 Mar. 2011

Protein 3D structure is one of the key factors in recognizing gene functions. The availability of protein structure data in Protein Data Bank (PDB) enables us to conduct gene function analysis. However, the molecules in the PDB, whose structures have been determined, are always not corresponding to a unique gene. That is to say, the mapping from gene to PDB is not one-to-one. Thus this uncertain property complicates the analysis and increases the difficulty of gene function analysis. In this paper, we attempt to tackle this challenging issue and we study the problem of predicting gene function from protein structures based on the gene-PDB mapping. We first obtain the gene-PDB mapping, which is important in representing a gene by the structure set of all its corresponding PDB molecules. We then define a new gene-gene similarity measurement based on the structure similarity between PDB molecules. We further show that this new measurement matches with gene functional similarity nicely. This means that the measurement we introduced here can be useful for gene function prediction. Numerical examples are given to demonstrate our claim.

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