Jiangang Shu

Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the users search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.

Privacy-Preserving Smart Similarity Search Based on Simhash over Encrypted Data in Cloud Computing

In recent years, due to the appealing features of cloud computing, more and more sensitive or private information has been outsourced onto the cloud. Although cloud computing provides convenience, privacy and security of data becomes a big concern. For protecting data privacy, it is desirable for the data owner to outsource sensitive data in encrypted form rather than in plain text. However, encrypted storage will hinder our legal access, e.g., searching function. To deal with this dilemma, a considerable number of searchable encryption schemes have been proposed in this field. However, almost all of existing schemes focus on keyword-based query rather than document-based query, which is a crucial requirement for real world application. In this paper, we propose a similarity search method for encrypted document based on simhash. Through our scheme, data users can find similar encrypted documents stored in cloud by submitting a query document. In order to scale well for large data sources, we build a triebased index to improve search efficiency in our solution. Through rigorous privacy analysis and experiment on realworld dataset, our scheme is secure and efficient.

Smart cloud search services: verifiable keyword-based semantic search over encrypted cloud data

With the increasing popularity of the pay-as-you- consume cloud computing paradigm, a large number of cloud services are pushed to consumers. One hand, it brings great convenience to consumers who use intelligent terminals; on the other hand, consumers are also facing serious difficulties that how to search the most suitable services or products from cloud. So how to enable a smart cloud search scheme is a critical problem in the consumer-centric cloud computing paradigm. For protecting data privacy, sensitive data are always encrypted before being outsourced. Although the existing searchable encryption schemes enable users to search over encrypted data, these schemes support only exact keyword search, which greatly affects data usability. Moreover, these schemes do not support verifiability of search result. In order to save computation cost or download bandwidth, cloud server only conducts a fraction of search operation or return a part of result, which is viewed as selfish and semi-honest-but-curious. So, how to enhance flexibility of encrypted cloud data while supporting verifiability of search result is a big challenge. To tackle the challenge, a smart semantic search scheme is proposed in this paper, which returns not only the result of keyword-based exact match, but also the result of keyword-based semantic match. At the same time, the proposed scheme supports the verifiability of search result. The rigorous security analysis and performance analysis show that the proposed scheme is secure under the proposed model and effectively achieves the goal of keyword-based semantic search.

New Forensic Methods for OOXML Format Documents

MS Office 2007–2013 documents, which use new Office Open XML (OOXML) format, could be illegally used as cover mediums to transmit secret information by offenders, because they do not easily arouse others suspicion. This paper proposes five forensic methods for OOXML format documents on the basis of researching the potential information hiding methods. The proposed forensic methods are classified into two groups to describe the details: document structure and document format. The aim is to provide security detection technology for electronic documents downloaded by users, and then prevent the damage of secret information embedded by offenders. Extensive experiments based on real data set demonstrate the effectiveness of the proposed methods.

An Effective Search Scheme Based on Semantic Tree Over Encrypted Cloud Data Supporting Verifiability

With the increasing popularity of cloud computing, more and more sensitive or private information is being outsourced to cloud server. For protecting data privacy, sensitive data are always encrypted before being outsourced. Although the existing searchable encryption schemes enable users to search over encrypted data, these schemes support only exact keyword search, which greatly affects data usability. Moreover, these schemes do not support verifiability of search result. To tackle the challenge, a smart semantic search scheme is proposed in this paper, which returns not only the result of keyword-based exact match, but also the result of keyword-based semantic match. At the same time, the proposed scheme supports the verifiability of search result.

A Similarity Search Method for Encrypted Cloud Document

The increasing popularity of cloud computing makes the data owner to outsource sensitive data in encrypted form onto the cloud. To tackle the problem of searching over encrypted cloud data, many keyword-based searchable encryption schemes have been proposed. However, there are few document-based search schemes in this field, which are also important in real applications. In this paper, we use the simhash algorithm to realizing similarity search over encrypted document with privacy-preserving. Through our method, data users can find similar encrypted documents stored in cloud by querying a document. In addition, a trie-based index is adopted in our solution to improve search efficiency.