Keyword Query over Error-Tolerant Knowledge Bases

With more and more knowledge provided by WWW, querying and mining the knowledge bases have attracted much research attention. Among all the queries over knowledge bases, which are usually modelled as graphs, a keyword query is the most widely used one. Although the problem of keyword query over graphs has been deeply studied for years, knowledge bases, as special error-tolerant graphs, lead to the results of the traditional defined keyword queries out of users＇ satisfaction. Thus, in this paper, we define a new keyword query, called confident r-clique, specific for knowledge bases based on the r-clique definition for keyword query on general graphs, which has been proved to be the best one. However, as we prove in the paper, finding the confident r-cliques is #P-hard. We propose a filtering-and-verification framework to improve the search efficiency. In the filtering phase, we develop the tightest upper bound of the confident r-clique, and design an index together with its search algorithm, which suits the large scale of knowledge bases well. In the verification phase, we develop an efficient sampling method to verify the final answers from the candidates remaining in the filtering phase. Extensive experiments demonstrate that the results derived from our new definition satisfy the users＇ requirement better compared with the traditional r-clique definition, and our algorithms are efficient.

Search recommendation model based on user search behavior and gradual forgetting collaborative filtering strategy

The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. By analyzing user＇s dynamic search behavior, the paper introduces a new method of using a keyword query graph to express user＇s dynamic search behavior, and uses Bayesian network to construct the prior probability of keyword selection and the migration probability between keywords for each user. To reflect the dynamic changes of the user＇s preference, the paper introduces non-lineal gradual forgetting collaborative filtering strategy into the personalized search recommendation model. By calculating the similarity between each two users, the model can do the recommendation based on neighbors and be used to construct the personalized search engine.

Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

Searchable symmetric encryption(SSE)has been introduced for secure outsourcing the encrypted database to cloud storage,while maintaining searchable features.Of various SSE schemes,most of them assume the server is honest but curious,while the server may be trustless in the real world.Considering a malicious server not honestly performing the queries,verifiable SSE(VSSE)schemes are constructed to ensure the verifiability of the search results.However,existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during verification.To address this challenge,we present an efficient VSSE scheme,built on OXT protocol(Cash et al.,CRYPTO 2013),for conjunctive keyword queries with sublinear search overhead.The proposed VSSE scheme is based on a privacy-preserving hash-based accumulator,by leveraging a well-established cryptographic primitive,Symmetric Hidden Vector Encryption(SHVE).Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations,thus greatly reducing the computational cost in the verification process.Besides,the proposed VSSE scheme can still provide a proof when the search result is empty.Finally,the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.

Answering Non-Answer Questions on Reverse Top-k Geo-Social Keyword Queries

Due to the wide-spread use of geo-positioning technologies and geo-social networks,the reverse top-k geo-social keyword query has attracted considerable attention from both industry and research communities.A reverse top-k geo-social keyword(RkGSK)query finds the users who are spatially near,textually similar,and socially relevant to a specified point of interest.RkGSK queries are useful in many real-life applications.For example,they can help the query issuer identify potential customers in marketing decisions.However,the query constraints could be too strict sometimes,making it hard to find any result for the RkGSK query.The query issuers may wonder how to modify their original queries to get a certain number of query results.In this paper,we study non-answer questions on reverse top-k geo-social keyword queries(NARGSK).Given an RkGSK query and the required number M of query results,NARGSK aim to find the refined RkGSK query having M users in its result set.To efficiently answer NARGSK,we propose two algorithms(ERQ and NRG)based on query relaxation.As this is the first work to address NARGSK to the best of our knowledge,ERQ is the baseline extended from the state-of-the-art method,while NRG further improves the efficiency of ERQ.Extensive experiments using real-life datasets demonstrate the efficiency of our proposed algorithms,and the performance of NRG is improved by a factor of 1–2 on average compared with ERQ.