Search

A combination of tools and methods known as "graph mining" is used to evaluate real-world graphs, forecast the potential effects of a given graph's structure and properties for various applications, and build models that can yield actual graphs that closely resemble the structure seen in real-world graphs of interest. However, some graph mining approaches possess scalability and dynamic graph challenges, limiting practical applications. In machine learning and data mining, among the unique methods is graph embedding, known as network representation learning where representative methods suggest encoding the complicated graph structures into embedding by utilizing specific pre-defined metrics. Co-occurrence graphs and keyword searches are the foundation of search engine optimizations for diverse real-world applications. Current work on keyword searches on graphs is based on pre-established information retrieval search criteria and does not provide semantic linkages. Recent works on co-occurrence and keyword search methods function effectively on graphs with only one layer instead of many layers. However, the graph neural network has been utilized in recent years as a branch of graph model due to its excellent performance. This paper proposes an Effective Keyword Search Co-occurrence Multi-Layer Graph mining method by employing two core approaches: Multi-layer Graph Embedding and Graph Neural Networks. We conducted extensive tests using benchmarks on real-world data sets. Considering the experimental findings, the proposed method enhanced with the regularization approach is substantially excellent, with a 10% increment in precision, recall, and f1-score. [ABSTRACT FROM AUTHOR]

To ensure both data security and usability in cloud environments simultaneously, Searchable Encryption (SE) emerges as a crucial technique. Through the utilization of Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy AttributeBased Keyword Search (CP-ABKS) scheme accomplishes keyword-based retrieval and fine-grained access control concurrently. However, existing CP-ABKS schemes with a single attribute authority entail costly user certificate verification and secret key distribution, leading to a performance bottleneck in distributed cloud systems. Thus, this study introduces a secure Multi-authority CPABKS (MABKS) system to overcome these challenges and alleviate the computation and storage burden on resource-limited devices within cloud systems. Furthermore, the MABKS system extends its functionality to support malicious attribute authority tracing and attribute updates. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyses Twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles and was able to successfully identify the political leniency of the user profiles with 99% confidence level, and also on a synthetic dataset with 2500 tweets, where the predicted accuracy and F1 score were 0.99 and 0.985 respectively, and 0.97 and 0.975 when neutral users were also considered for classification. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in the number of users belonging to the different clusters. [ABSTRACT FROM AUTHOR]

Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer from privacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharing scheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computational overhead of encryption and decryption reaches a lightweight constant level, and supports keyword search and policy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technology is utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the data to achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions (SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology is utilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflect the obvious advantages of the scheme in terms of encryption and decryption computation overhead and storage overhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solution for the safe and efficient sharing of EMR. [ABSTRACT FROM AUTHOR]

Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search.

The widespread adoption of online social networks has facilitated content sharing among users. However, privacy controls restrict users' access to only a limited portion of the network, typically limited to direct connections or two-hop friends. Browsing relevant profiles and home pages has become a common practice for users, but the vast amount of data involved often hampers their ability to efficiently retrieve the desired information. This paper presents an efficient keyword search model designed to aid users in accessing the required information effectively. Leveraging advancements in Bloom filter technology, we propose a novel summary index called Friend-based Bloom filter (FBF), which enables large-scale full-text retrieval while reducing inter-server communication costs and query latency. We conduct a comprehensive simulation to evaluate our ranking model, and the results demonstrate the effectiveness of the FBF scheme. Specifically, our approach achieves a reduction of 92.4% in inter-server communication costs and 78.7% in query latency, with a high-search precision condition resulting in a remarkable 98.3% improvement.

Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users' search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user's search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user's intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user's intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model's proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users' search intent as well as the quality of classification during search. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spoken keyword detection is a technique used to identify specific keywords or phrases in spoken language. It is often used in the field of speech recognition to trigger specific actions or responses. For example, a spoken keyword detection system might be used to activate a voicecontrolled assistant or to initiate a search query. Keyword detection systems can be trained to recognize a wide range of keywords, depending on the specific needs of the application. They may use machine learning techniques to analyze the audio signal and identify patterns that correspond to specific keywords. [ABSTRACT FROM AUTHOR]

In recent years, there has been rapid development in computer technology, leading to an increasing number of medical systems utilizing electronic medical records (EMRs) to store their clinical data. Because EMRs are very private, healthcare institutions usually encrypt these data before transferring them to cloud servers. A technique known as searchable encryption (SE) can be used by healthcare institutions to encrypt EMR data. This technique enables searching within the encrypted data without the need for decryption. However, most existing SE schemes only support keyword or range searches, which are highly inadequate for EMR data as they contain both textual and digital content. To address this issue, we have developed a novel searchable symmetric encryption scheme called SSE-RK, which is specifically designed to support both range and keyword searches, and it is easily applicable to EMR data. We accomplish this by creating a conversion technique that turns keywords and ranges into vectors. These vectors are then used to construct index tree building and search algorithms that enable simultaneous range and keyword searches. We encrypt the index tree using a secure K-Nearest Neighbor technique, which results in an effective SSE-RK approach with a search complexity that is quicker than a linear approach. Theoretical and experimental study further demonstrates that our proposed scheme surpasses previous similar schemes in terms of efficiency. Formal security analysis demonstrates that SSE-RK protects privacy for both data and queries during the search process. Consequently, it holds significant potential for a wide range of applications in EMR data. Overall, our SSE-RK scheme, which offers improved functionality and efficiency while protecting the privacy of EMR data, generally solves the shortcomings of the current SE schemes. [ABSTRACT FROM AUTHOR]

With the increasing of requirements from many aspects, various queries and analyses arise focusing on social network. Time is a common and necessary dimension in various types of social networks. Social networks with time information are called temporal social networks, in which time information can be the time when a user sends message to another user. Keywords search in temporal social networks consists of finding relationships between a group users that has a set of query labels and is valid within the query time interval. It provides assistance in social network analysis, classification of social network users, community detection, etc. However, the existing methods have limitations in solving temporal social network keyword search problems. We propose a basic algorithm, the discrete timestamp algorithm, with the intention of turning the problem into a traditional keyword search on social networks. We also propose an approximative algorithm based on the discrete timestamp algorithm, but it still suffers from the traditional algorithms' low efficiency. To further improve the performance, we propose a new algorithm based on dynamic programming to solve the keyword search in temporal social network. The main idea is to extend a vertex into a solution by edge-growth operation and tree-merger operation. We also propose two powerful pruning techniques to reduce the intermediate results during the extension. Additionally, all of the algorithms we proposed are capable of handling a variety of ranking functions, and all of them can be made to conform to top-N keyword querying. The efficiency and effectiveness of the proposed algorithms are verified through extensive empirical studies. [ABSTRACT FROM AUTHOR]

Abstract With the increasing of requirements from many aspects, various queries and analyses arise focusing on social network. Time is a common and necessary dimension in various types of social networks. Social networks with time information are called temporal social networks, in which time information can be the time when a user sends message to another user. Keywords search in temporal social networks consists of finding relationships between a group users that has a set of query labels and is valid within the query time interval. It provides assistance in social network analysis, classification of social network users, community detection, etc. However, the existing methods have limitations in solving temporal social network keyword search problems. We propose a basic algorithm, the discrete timestamp algorithm, with the intention of turning the problem into a traditional keyword search on social networks. We also propose an approximative algorithm based on the discrete timestamp algorithm, but it still suffers from the traditional algorithms’ low efficiency. To further improve the performance, we propose a new algorithm based on dynamic programming to solve the keyword search in temporal social network. The main idea is to extend a vertex into a solution by edge-growth operation and tree-merger operation. We also propose two powerful pruning techniques to reduce the intermediate results during the extension. Additionally, all of the algorithms we proposed are capable of handling a variety of ranking functions, and all of them can be made to conform to top-N keyword querying. The efficiency and effectiveness of the proposed algorithms are verified through extensive empirical studies.

The widespread adoption of cloud infrastructures has revolutionized data storage and access. However, it has also raised concerns regarding the privacy of sensitive data. To address these concerns, encryption techniques have been widely used. However, traditional encryption schemes limit the efficient search and retrieval of encrypted data. To tackle this challenge, innovative approaches have emerged, such as the utilization of Homomorphic Encryption (HE) in Searchable Encryption (SE) schemes. This paper provides a comprehensive analysis of the advancements in HE-based privacy-preserving techniques, focusing on their application in SE. The main contributions of this work include the identification and classification of existing SE schemes that utilize HE, a comprehensive analysis of the types of HE used in SE, an examination of how HE shapes the search process structure and enables additional functionalities, and the identification of promising directions for future research in HE-based SE. The findings reveal the increasing usage of HE in SE schemes, particularly Partially Homomorphic Encryption. The popularity of this type of HE schemes, especially Paillier's cryptosystem, can be attributed to its simplicity, proven security properties, and widespread availability in open-source libraries. The analysis also highlights the prevalence of index-based SE schemes using HE, the support for ranked search and multi-keyword queries, and the need for further exploration in functionalities such as verifiability and the ability to authorize and revoke users. Future research directions include exploring the usage of other encryption schemes alongside HE, addressing omissions in functionalities like fuzzy keyword search, and leveraging recent advancements in Fully Homomorphic Encryption schemes. [ABSTRACT FROM AUTHOR]

Data management services provided by the public cloud can economize the enterprise’s local storage costs, and meanwhile, realize data sharing among the enterprise. Corporate users, however, usually have concerns about the security and privacy of their data stored in the public cloud. Searchable encryption has been used as a secure method for enterprise users in the public cloud to share data via keyword search for many years. Nevertheless, conventional search and encryption primitives are challenging to realize several critical functions flexibly, such as key update, user revocation, lightweight computation, and low communication overhead for users, which will impede their widespread application. Besides, it may cause single-key-exposure security concerns by using untrusted devices. To address these issues, we present a dynamic consensus committee-based for secure data sharing with authorized multi-receiver searchable encryption (called DCC-SE), which exploits the blockchain dynamic committee to eliminate a single-key-exposure problem and enables fast keyword search without pairing and dynamic user management. The proposed DCC-SE scheme enjoys minor computation and communication overheads. It maintains a constant ciphertext size as the receiver increases. Furthermore, it periodically supports secure key and ciphertext updating and has been proven secure against chosen plaintext attacks and the chosen keyword guessing attacks under the random oracle model. The performance evaluation results show that DCC-SE has more feasibility and higher efficiency than the previous public key encryption with keyword search (PEKS) schemes through theoretical analysis and simulation studies. [ABSTRACT FROM AUTHOR]

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. They take a keyword query, locate their corresponding elements in the target database, and connect them using information on PK/FK constraints. Although there are many such systems in the literature, most of them only support queries with keywords referring to the contents of the database and just very few support queries with keywords refering the database schema. We propose Lathe, a novel R-KwS that supports such queries. To this end, we first generalize the well-known concepts of Candidate Joining Networks (CJNs) and Query Matches (QMs) to handle keywords referring to schema elements and propose new algorithms to generate them. Then, we introduce two major innovations: a ranking algorithm for selecting better QMs, yielding the generation of fewer but better CJNs, and an eager evaluation strategy for pruning void useless CJNs. We present experiments performed with query sets and datasets previously experimented with state-of-theart R-KwS systems. Our results indicate that Lathe can handle a wider variety of queries while remaining highly effective, even for databases with intricate schemas.

More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g., points of interest, flickr photos), or linked entities in semantic graphs (e.g., Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this article, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. In addition, we propose pre-processing, pruning, and approximate computation techniques that their combination reduces the computational cost of the algorithms even further. We theoretically analyze the approximation quality of our approaches. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification. [ABSTRACT FROM AUTHOR]

The convenience and efficiency of cloud computing have brought great potential for its development.More and more enterprises and individuals obtain real benefits by using various outsourcing services provided by cloud computing.In order to protect the confidentiality and integrity of outsourced data in the cloud,the keyword search over encrypted cloud data with privacy protection and integrity verification is becoming a research hotspot in the field of cloud computing.In this paper,we focus on the issue of the verifiable keyword search over encrypted data.The system models,threat models and frameworks adopted in the existing works are firstly introduced.Related works are overviewed from the aspects of verifiable single keyword search and verifiable multi-keyword search over encrypted data,and the ideas of these works are briefly described together with the advantages and disadvantages.At last,the conclusion is presented through a comprehensive analysis and comparison of the related works,and the possible research directions and trends in the future are prospected.

Many breakthroughs on security and privacy-preserving techniques have emerged to mitigate the trust loss of cloud hosting environment caused by various types of attacks. To enhance memory-level security of multi-keyword fuzzy search, a widely occurred query request, we take the initiative to apply Trusted Execution Environment (a.k.a TEE) technology to our protocol design which provides hardware-based tamper-proof enclaves. Then we propose the Edit Distance-based Obfuscation Mechanism to further protect the query process executed outside TEE against access pattern leakage. With concerns of practicality and performance, we also propose the two-layer fuzzy index structure and Trend-aware Cache. The former addresses the space limitation of TEE memory for searching large datasets, while the latter optimizes the cache utility of TEE with trend-aware coordinator to effectively reduce the communication overhead. [ABSTRACT FROM AUTHOR]

In cloud computing, documents can be outsourced to the cloud server to achieve flexible access control and efficient sharing among multiple users. The outsourced documents can be intelligently searched according to some keywords with the help of cloud server. During the search process, some private information of outsourced documents may be leaked since the keywords may contain sensitive information of users. However, existing privacy-preserving keyword search schemes have high computation complexity, which are not suitable for resource-constrained end devices—that is, the data processing and search trapdoor generation procedures require users to take resource-intensive computations, e.g., high-dimensional matrix operations, exponentiations and bilinear pairings, which are unaffordable by resource-constrained devices. To address the issues of efficiency and privacy for realizing sorted multi-keyword search over outsourced data in clouds, this paper proposes a lightweight privacy-preserving ranked multi-keyword search (PRMS) scheme, which is further extended to allow each outsourced document to be associated with multiple types of keywords. The searched documents can be sorted according to their similarity scores between the search query and the keyword index of documents, so that only when the similarity score exceeds a given threshold, the corresponding searched document will be returned. The security analysis demonstrates that the proposed PRMS schemes can guarantee the privacy of outsourced documents and keywords, and provides unlinkability for search trapdoors. Performance analysis and comparison show the practicality of the proposed PRMS schemes. [ABSTRACT FROM AUTHOR]

Verifiable symmetric searchable encryption is a keyword search technology that supports verification of search results. Many schemes improve search performance by dividing each keyword label into segments and storing them in a Trie-tree at the expense of high storage. And the index will degenerate into a linear linked list when all keyword labels have the same prefix except for the last segment. But it will greatly affect the search efficiency. In this paper, we propose a verifiable symmetric searchable encryption scheme based on the AVL Tree (abbreviated as VSSE-AVL), which uses complete keyword labels to build the index. Compared with the Trie-tree index, VSSE-AVL not only balances storage and search performance, but also avoids degradation. To verify the correctness and completeness of empty search results, we store path information in each leaf node and node with only one child node. Considering the substitution attack, we bind the file identifier and the file so that the client will find out once the server returns inconsistent search results. Rigorous security analysis shows VSSE-AVL satisfies privacy and verifiability. Compared with the verifiable SSE-2 with the same security, the experimental evaluation shows that our proposed scheme performs better on storage, search and verification. [ABSTRACT FROM AUTHOR]

Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach. [ABSTRACT FROM AUTHOR]

To protect the privacy of power data, we usually encrypt data before outsourcing it to the cloud servers. However, it is challenging to search over the encrypted data. In addition, we need to ensure that only authorized users can retrieve the power data. The attribute-based searchable encryption is an advanced technology to solve these problems. However, many existing schemes do not support large universe, expressive access policies, and hidden access policies. In this paper, we propose an attribute-based keyword search encryption scheme for power data protection. Firstly, our proposed scheme can support encrypted data retrieval and achieve fine-grained access control. Only authorized users whose attributes satisfy the access policies can search and decrypt the encrypted data. Secondly, to satisfy the requirement in the power grid environment, the proposed scheme can support large attribute universe and hidden access policies. The access policy in this scheme does not leak private information about users. Thirdly, the security analysis and performance analysis indicate that our scheme is efficient and practical. Furthermore, the comparisons with other schemes demonstrate the advantages of our proposed scheme.

In recent years, there has been rapid development in computer technology, leading to an increasing number of medical systems utilizing electronic medical records (EMRs) to store their clinical data. Because EMRs are very private, healthcare institutions usually encrypt these data before transferring them to cloud servers. A technique known as searchable encryption (SE) can be used by healthcare institutions to encrypt EMR data. This technique enables searching within the encrypted data without the need for decryption. However, most existing SE schemes only support keyword or range searches, which are highly inadequate for EMR data as they contain both textual and digital content. To address this issue, we have developed a novel searchable symmetric encryption scheme called SSE-RK, which is specifically designed to support both range and keyword searches, and it is easily applicable to EMR data. We accomplish this by creating a conversion technique that turns keywords and ranges into vectors. These vectors are then used to construct index tree building and search algorithms that enable simultaneous range and keyword searches. We encrypt the index tree using a secure K-Nearest Neighbor technique, which results in an effective SSE-RK approach with a search complexity that is quicker than a linear approach. Theoretical and experimental study further demonstrates that our proposed scheme surpasses previous similar schemes in terms of efficiency. Formal security analysis demonstrates that SSE-RK protects privacy for both data and queries during the search process. Consequently, it holds significant potential for a wide range of applications in EMR data. Overall, our SSE-RK scheme, which offers improved functionality and efficiency while protecting the privacy of EMR data, generally solves the shortcomings of the current SE schemes.

Searchable encryption technology enables users to access data that has been made publicly encrypted without divulging the original content. The majority of the currently available multi-user certificateless searchable encryption technologies are based on identity-based public key encryption as well as conventional public key cryptosystems. Thus, they are challenged to adapt to the security needs of today's large-scale network computing environment. As a result, issues such as excessive overhead, poor security, and the inability to handle large-scale applications are unavoidable. In order to address the aforementioned issues, this paper uses the method of combining public key authentication encryption and searchable encryption to propose a certificateless searchable encryption scheme in multi-user circumstances. The stochastic prediction model demonstrates that the scheme can effectively fend off keyword guessing attacks. The proposed algorithm not only performs well in terms of computation but also significantly reduces the amount of computation in simulations. [ABSTRACT FROM AUTHOR]

Cloud storage services have grown extensively in recent years. For security and privacy purposes, sensitive data need to be outsourced to clouds in encrypted form. Searchable public key encryption (SPKE) enables data ciphertexts to be retrieved by keyword(s) without decryption. Unfortunately, most of the existing SPKE schemes cannot withstand the keyword guessing attack. To combat such attack, public key authenticated encryption with keyword search (PAEKS) was presented. However, the existing PAEKS schemes were proven secure under a designated-targets security model, in which an adversary only can attack a sender and a recipient designated by the challenger. Our cryptanalysis indicates that such a scheme may be insecure against the practical attacks where the adversaries choose their targets by themselves. To fight against adaptively-chosen-targets adversaries, we refine the adversary model for PAEKS by permitting the adversaries to choose their targets adaptively, and then formalize the security definitions under the improved security model. After that, we devise a lightweight PAEKS scheme that avoids the time-consuming bilinear pairing operations and give the security proofs. The comparisons show that it outperforms the existing bilinear pairing-based PAEKS schemes in both the computation and communication performance, and therefore is more suitable for the resource-constrained mobile devices. [ABSTRACT FROM AUTHOR]

In a highly evolved big data era, intelligent data analysis can improve social operation efficiency and save resources. However, it also brings masses of conflicts, such as malicious mining and abuse of personal privacy information. This paper introduces a privacy protection scheme for social applications. In this scheme, attribute based searchable encryption is used to defend the security of confidential data and ensure the availability of data. Moreover, the access control structure of ciphertext strategy can meet the needs of data sharing in social applications. Security analysis shows that the scheme does not disclose privacy information in index ciphertext, search trapdoor, and equality test. Compared with plaintext information upload and sharing, the additional performance overhead caused by the scheme is acceptable. The scheme can be actually deployed in social applications. [ABSTRACT FROM AUTHOR]

This paper presents a multi-proxy (2 proxies) based encrypted keyword search scheme that enjoys the following properties: This scheme provides data confidentially that encrypted data does not leak any keywords and documents to the attackers (data server and a proxy). Moreover, the proposed scheme provides trapdoor privacy. In other words, the attackers do not learn any information about searched keywords. Furthermore, even if a proxy is controlled by an attacker, the attacker does not learn any information about the queries (keywords that the user searches over the database) and database results. Different from other studies, this scheme provides lightweight user-side query and data processing. In other words, most of the job (query processing) is done by the proxies on behalf of the user. Finally, the proposed scheme relaxes the trust assumption that eliminates a single point of failure by introducing multi-proxy architecture. [ABSTRACT FROM AUTHOR]

In the past decade,end-to-end automatic speech recognition (ASR) frameworks have developed rapidly.End-to-end ASR has shown not only very different characteristics from traditional ASR based on hidden Markov models (HMMs),but also advanced performances.Thus,end-to-end ASR is being more and more popular and has become another major type of ASR frameworks.A keyword search (KWS) framework based on end-to-end ASR and frame-synchronous alignment is proposed for solving the problem that end-to-end ASR cannot provide accurate keyword timestamps and confidence scores,and experimental verification on a Vietnamese dataset is made.First,utterances are decoded by an end-to-end Uyghur ASR system,obtaining N-best hypotheses.Next,a dynamic programming-based alignment algorithm is implemented on each of these ASR hypotheses and per-frame phoneme probabilities,which are provided by a phoneme classifier jointly trained with the ASR model,to compute time stamps and confidence scores for each word in N-best hypotheses.Then,final KWS result is obtained by detecting keywords within N-best hypotheses and removing duplicated keyword occurrences according to time stamps and confident scores.Experimental results on a Vietnamese conversational telephone speech dataset show that the proposed KWS system achieves an F1 score of 77.6%,which is relatively 7.8% higher than the F1 score of the traditional HMM-based KWS system.The proposed system also provides reliable keyword confidence scores.

This paper presents the resources and benchmarks developed for keyword search (KWS) in spoken audio from six low-resource Indian languages (from two families), namely Gujarati, Hindi, Marathi, Odia, Tamil, and Telugu. The current work on constructing keywords and building benchmark KWS systems is inspired by the popular IARPA Babel program and the subsequent works on low-resource KWS. The keywords are constructed by taking into account their properties i.e., occurrence, length, and average confusability; and their effects on the evaluation metric - the term-weighted value (TWV). We make use of freely available speech datasets, and reprocess them to create resources for KWS, thereby adding value to the existing speech resources. Four ASR-based KWS systems are built, and their performance is analyzed across the three keyword properties on all the six languages. The prepared keywords and other related resources to replicate our experiments are made available for the public. We believe that the analysis and guidelines provided in this paper will not only help the research community, but also practitioners and engineers to easily create KWS resources for newer languages, datasets, and scenarios.