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Nowadays, people widely use Searchable Symmetric Encryption (SSE) to design the equality query and range query operation for encrypted database systems. A recent attack points out forward privacy is the necessary security goal which, however, has not been achieved by most of SSE schemes. Motivated by this security goal, we propose a forward secure SSE scheme whose index structure consists of keyed-block chains. The proposed scheme is efficient since the keyed-block chain can be implemented by symmetric encryption primitive and the scheme also supports both add and delete operations in one instance. We further employ the one-way permutation function to reduce the storage on the client side. Experiments show that our scheme is 4 × , 300 × and 300 × faster than the state-of-the-art forward private SSE scheme (CCS 2016) in search, update and token generation, respectively. [ABSTRACT FROM AUTHOR]

• We propose an efficient proxy-oriented identity-based encryption with keyword search scheme (PO-IBEKS) from lattices. • PO-IBEKS enables an original data owner authorizes a proxy to encrypt sensitive data as well as corresponding keywords and upload them to clouds. • PO-IBEKS can resist against insider keyword guessing attacks (IKGA). • PO-IBEKS can achieve ciphertext indistinguishability, existential unforgeability, and delegation security. • PO-IBEKS is much more practical in post-quantum secure cloud storage systems compared with existing schemes. Public-key encryption with keyword search (PEKS) enables users to search over encrypted data and retrieve target data efficiently. However, most of existing PEKS schemes are vulnerable to adversaries equipped with quantum computers in the near future, and even incur complex certificate management procedures due to the public key infrastructure (PKI). To this end, we propose a proxy-oriented identity-based encryption with keyword search (PO-IBEKS) scheme from lattices for cloud storage, which is post-quantum secure. In PO-IBEKS, an original data owner authorizes a proxy to encrypt sensitive data as well as corresponding keywords and upload ciphertexts to clouds, which alleviates the data processing burden on the original data owner. Besides, PO-IBEKS can resist inside keyword guessing attacks (IKGA) from misbehaved cloud servers by integrating the learning with errors (LWE) encryption and preimage sampleable function. Each entity in PO-IBEKS is identified with her/his recognizable information, thereby eliminating managing certificates. Formal security analysis proves that PO-IBEKS can achieve ciphertext indistinguishability, existential unforgeability, and delegation security. Experimental results demonstrate PO-IBEKS is much more practical when compared with existing schemes. [ABSTRACT FROM AUTHOR]

Abstract In encrypted email system, how to search over encrypted cloud emails without decryption is an important and practical problem. Public key encryption with keyword search (PEKS) is an efficient solution to it. However, PEKS suffers from the complex key management problem in the public key infrastructure. Its variant in the identity-based setting addresses the drawback, however, almost all the schemes does not resist against offline keyword guessing attacks (KGA) by inside adversaries. In this work we introduce the notion of designated-server identity-based authenticated encryption with keyword search (dIBAEKS), in which the email sender authenticates the message while encrypting so that no adversary including the server can launch offline KGA. Furthermore, we strengthen the security requirement so that only the designated server has the capability to search over encrypted emails for receivers. We formally define dIBAEKS and its security models, and propose two dIBAEKS constructions using Type-I and Type-III bilinear pairing, respectively. We compare our schemes with some related IBEKS schemes in the literature, and do experiments to demonstrate its efficiency. Although they are slightly less computationally efficient than but still comparable with the related schemes, our schemes provide stronger security guarantee and better protect users' privacy. [ABSTRACT FROM AUTHOR]

Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers. In this paper, we propose CI-Rank , a new approach to keyword search in databases, which considers the importance of individual nodes in a query answer and the cohesiveness of the result structure in a balanced way. CI-Rank is built upon a carefully designed model called Random Walk with Message Passing that helps capture the relationships between different nodes in the query answer. This model lends itself well to adaptation to user preferences. We develop a branch and bound algorithm to support the efficient generation of top- k query answers. Indexing methods are also introduced to further speed up the run-time processing of queries. Extensive experiments conducted on two real data sets with a real user query log confirm the effectiveness and efficiency of CI-Rank . [ABSTRACT FROM AUTHOR]

Keyword Search has been recognised as a viable alternative for information search in semi-structured and structured data sources. Current state-of-the-art keyword-search techniques over relational databases do not take advantage of correlative meta-information included in structured and semi-structured data sources leaving relevant answers out. These techniques are also limited due to scalability, performance and precision issues that are evident when they are implemented on large datasets. Based on an in-depth analysis of issues related to indexing and ranking semi-structured and structured information. We propose a new keyword-search algorithm that takes into account the semantic information extracted from the schemes of the structured and semi-structured data sources and combine it with the textual relevance obtained by a common text retrieval approach. The algorithm is implemented in a keyword-based search engine called KESOSASD (Keyword Search Over Semi-structured and Structured Data) , improving its precision and response time. Our approach models the semi-structured and structured information as graphs, and make use of a Virtual Document Structure Aware Inverted Index (VDSAII) . This index is created from a set of logical structures called Virtual Documents , which capture and exploit the implicit structural relationships (semantics) depicted in the schemas of the structured and semi-structured data sources. Extensive experiments were conducted to demonstrate that KESOSASD outperforms existing approaches in terms of search efficiency and accuracy. Moreover, KESOSASD is prepared to scale out and manage large databases without degrading its effectiveness. [ABSTRACT FROM AUTHOR]

Abstract: We present a family of character-based index schemes for wildcard search in peer-to-peer networks. The supported query format allows the two wildcard matching operators ‘?’ and ‘*’ to be used arbitrarily. To our knowledge, no distributed solution for this general form of wildcard search has been proposed thus far. Moreover, the schemes can achieve balanced loads, avoid hot-spots and single points of failure, reduce storage and maintenance costs, and offer ranking mechanisms for matching objects. The main focus is on a thorough comparative study on these index schemes from the following perspectives: query expressiveness, encoding effectiveness, load balance, and search performance. [Copyright &y& Elsevier]

Abstract: Keyword search is a user-friendly mechanism used to retrieve XML data for web and scientific applications. Unlike text data, XML data contain rich semantics, which are obviously useful for information retrieval. It is observed that most existing approaches for XML keyword search either do not consider relevance ranking or perform relevance ranking using traditional text IR techniques. Based on an in-depth analysis of user information need and XML structural semantics, we propose to rank the relevance between a keyword query and an XML fragment by their semantic similarity. We first present a formula to quantify the concept of semantic similarity and then introduce a novel semantic ranking scheme for XML keyword search. Our extensive experiments demonstrate that the proposed scheme outperforms existing approaches in terms of search quality and achieve high efficiency and scalability. [Copyright &y& Elsevier]

Abstract: In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let’s assume that a research or financial analyst is searching for information on a topic, continuously polling data from multiple (and possibly heterogeneous) text streams, such as RSS feeds, blogs, etc. The topic of interest can be described with the aid of several keywords. Current filtering approaches would just identify single text streams containing some of the keywords. However, it would be more flexible and powerful to search across multiple streams, which may collectively answer the analyst’s question. We present such model that takes in consideration the continuous flow of text in streams and uses efficient pipelined algorithms such that results are output as soon as they are available. The proposed model is evaluated analytically and experimentally, where the Enron dataset and a variety of blog datasets are used for our experiments. [Copyright &y& Elsevier]

The article discusses some technology advances and data science that are useful for lawyers in fact investigation and document discovery in trial practice, such as keyword searching and predictive coding, the use of metadata and entity data, and advancements in machine learning.