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153 results on '"Beixing Deng"'

49. Learning with error based searchable encryption scheme

Author

Beixing Deng, Jiuling Zhang, and Xing Li

Subjects
Triple DES, Theoretical computer science, Computer science, Data_CODINGANDINFORMATIONTHEORY, Affine cipher, Probabilistic encryption, Running key cipher, Data_GENERAL, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Electrical and Electronic Engineering, Stream cipher, Null cipher, Block cipher, Transposition cipher
Abstract

A learning with error problem based encryption scheme that allows secure searching over the cipher text is proposed. Both the generation of cipher text and the trapdoor of the query are based on the problem of learning with errors. By performing an operation over the trapdoor and the cipher text, it is able to tell if the cipher text is the encryption of a plaintext. The secure searchable encryption scheme is both cipher text and trapdoor indistinguishable. The probabilities of missing and failing match occurrence in searching are both exponentially small.

Published
2012
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50. Using the Dempster-Shafer theory of evidence to rank documents

Author

Xing Li, Beixing Deng, and Jiuling Zhang

Subjects
Multidisciplinary, Information retrieval, Basic probability, Computer science, Dempster–Shafer theory, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Rank (computer programming), Theory of evidence, Vector space model, Relevance (information retrieval), Text Retrieval Conference
Abstract

Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster's rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference (TREC) test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model (VSM). Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision.

Published
2012
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