Zhangjie Fu

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.

Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement

Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al., who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wangs scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wangs scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al.s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al.s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.

A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment

A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large-scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial-temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel-based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi-objective algorithm called time and space optimized NSGA-II TS-NSGA-II. Experiment results have shown that compared with the original load-balancing scheme, our approach can save approximate 47-55i¾źs averagely on each task execution. Simultaneously, 1.254i¾ź of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6% improvement over the original scheme. Copyright

Enabling Semantic Search based on Conceptual Graphs over Encrypted Outsourced Data

Currently, searchable encryption is a hot topic in the field of cloud computing. The existing achievements are mainly focused on keyword-based search schemes, and almost all of them depend on predefined keywords extracted in the phases of index construction and query. However, keyword-based search schemes ignore the semantic representation information of users’ retrieval and cannot completely match users’ search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, for the first time, we define and solve the problems of semantic search based on conceptual graphs (CGs) over encrypted outsourced data in clouding computing (SSCG). We first employ the efficient measure of “sentence scoring” in text summarization and Tregex to extract the most important and simplified topic sentences from documents. We then convert these simplified sentences into CGs. To perform quantitative calculation of CGs, we design a new method that can map CGs to vectors. Next, we rank the returned results based on “text summarization score”. Furthermore, we propose a basic idea for SSCG and give a significantly improved scheme to satisfy the security guarantee of searchable symmetric encryption (SSE). Finally, we choose a real-world dataset, i.e., the CNN dataset to test our scheme. The results obtained from the experiment show the effectiveness of our proposed scheme.

Towards efficient content-aware search over encrypted outsourced data in cloud

With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.

Privacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over Encrypted Outsourced Data

Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users’ search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.

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.

Forensic investigation of OOXML format documents

MS Office documents could be illegally copied by offenders, and forensic investigators still face great difficulty in investigating and tracking the source of these illegal copies. This paper mainly proposes a forensic method based on the unique value of the revision identifier (RI) to determine the source of suspicious electronic documents. This method applies to electronic documents which use Office Open XML (OOXML) format, such as MS Office 2007, Mac Office 2008 and MS Office 2010. According to the uniqueness of the RI extracted from documents, forensic investigators can determine whether the suspicious document and another document are from the same source. Experiments demonstrate that, for a copy of an electronic document, even if all the original characters are deleted or formatted by attackers, forensic examiners can determine that the copy and the original document are from the same source through detecting the RI values. Additionally, the same holds true if attackers just copy some characters from the original document to a newly created document. As long as there is one character left whose original format has not been cleared, forensic examiners can determine that the two documents are from the same source using the same method. This paper also presents methods for OOXML format files to detect the time information and creator information, which can be used to determine who the real copyright holder is when a copyright dispute occurs.

Enabling Central Keyword-Based Semantic Extension Search Over Encrypted Outsourced Data

In practice, search keywords have quite different importance when users take search operations. In addition, such keywords may have a certain grammatical relationship among them, which reflect the importance of keywords from the user’s perspective intuitively. However, the existing search techniques regard the search keywords as independent and unrelated. In this paper, for the first time, we take the relation among query keywords into consideration and design a keyword weighting algorithm to show the importance of the distinction among them. By introducing the keyword weight to the search protocol design, the search results will be more in line with the user’s demand. On top of this, we further design a novel central keyword semantic extension ranked scheme. By extending the central query keyword instead of all keywords, our scheme makes a good tradeoff between the search functionality and efficiency. To better express the relevance between queries and files, we further introduce the TF-IDF rule when building trapdoors and the index. In particular, our scheme supports both data set and keywords updates by using the sub-matrix technique. Our work first gives a basic idea for the design of the central keyword semantic extension ranked scheme, and then presents two secure searchable encryption schemes to meet different privacy requirements under two different threat models. Experiments on the real-world data set show that our proposed schemes are efficient, effective, and secure.