Semantic-Aware Searching Over Encrypted Data for Cloud Computing

Fu, Zhangjie; Xia, Lili; Sun, Xingming; Liu, Alex X.; Xie, Guowu

doi:10.1109/tifs.2018.2819121

Cited by 72 publications

(33 citation statements)

References 51 publications

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“…Fu et al [27] used the concept map to implement the semantic search of encrypted data. Fu et al [28], [29] proposed a content-based search scheme that implements an effective search for semantic perception on encrypted data.…”

Section: Semantic Searchmentioning

confidence: 99%

“…Therefore, the document collection and index need to be encrypted before uploading to the public cloud server, and should prevent the statistical analysis of the public cloud server. Additionally, we assume that there is no collusion between two cloud servers, which is adopted by most previous works [28], [35]. Our paper mainly discusses the known ciphertext model, in which the public cloud server can only obtain ciphertexts, security indexes, search results, and trapdoor.…”

Section: A Threat Modelmentioning

confidence: 99%

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FSSE: An Effective Fuzzy Semantic Searchable Encryption Scheme Over Encrypted Cloud Data

et al. 2020

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Currently, searchable encryption has attracted considerable attention in the field of cloud computing. The existing research mainly focuses on keyword-based search schemes, most of which support the exact matching of keywords. However, keyword-based search schemes ignore spelling errors and semantic expansions of keywords. The significant drawback makes the existing techniques unsuitable in cloud computing as it greatly affects system usability and can not completely satisfy the users' search intentions. In this paper, we propose an effective fuzzy semantic searchable encryption scheme (FSSE) that supports multi-keyword search over encrypted data in cloud computing. In our scheme, we exploit a keyword fingerprint generation algorithm to generate a fingerprint set of the keyword dictionary and a fingerprint of the query keywords, and employ Hamming distance to quantify keywords similarity. Based on the proposed fingerprint generation algorithm and Hamming distance, we realize fuzzy search. Furthermore, we utilize the semantic expansion technique to expand query keywords and calculate the semantic similarity between the query keywords and the expanded word of the query keywords to achieve the semantic search. To improve the search efficiency, we construct an inverted index structure and use the vector intersection matching as well as short-circuit matching operations to effectively filter irrelevant documents. The theoretical analysis and experimental results demonstrate that our proposed scheme satisfies the security guarantee of searchable encryption, enhances system usability, and is more efficient in comparison with the state of the art schemes. INDEX TERMS Searchable encryption, cloud computing, fuzzy semantic search, multi-keyword search. I. INTRODUCTION

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Section: Semantic Searchmentioning

confidence: 99%

Section: A Threat Modelmentioning

confidence: 99%

FSSE: An Effective Fuzzy Semantic Searchable Encryption Scheme Over Encrypted Cloud Data

et al. 2020

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“…Since then, many variants were proposed by adapting the index structure presented by Curtmola et al and Cash and Kamara or inventing new ones. There are schemes that provide richer functionalities, such as Boolean search [27], dynamic Boolean search [28], ranked search [29], [30] as well as semantic-aware scheme [31]. There are also schemes providing better security with less leakage and forward privacy [32], hide access pattern using private information retrieval in a two-server setting for large-scale database [33], as well as efficient blind storage where the server acts only as a commodity server offering only upload and download services [34].…”

Section: Queries On Encrypted Datamentioning

confidence: 99%

PrivHome: Privacy-Preserving Authenticated Communication in Smart Home Environment

Poh

Gope

Ning

2019

IEEE Trans. Dependable and Secure Comput.

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A smart home enables users to access devices such as lighting, HVAC, temperature sensors, and surveillance camera. It provides a more convenient and safe living environment for users. Security and privacy, however, is a key concern since information collected from these devices are normally communicated to the user through an open network (i. e. Internet) or system provided by the service provider. The service provider may store and have access to these information. Emerging smart home hubs such as Samsung SmartThings and Google Home are also capable of collecting and storing these information. Leakage and unauthorized access to the information can have serious consequences. For example, the mere timing of switching on/off of an HVAC unit may reveal the presence or absence of the home owner. Similarly, leakage or tampering of critical medical information collected from wearable body sensors can have serious consequences. Encrypting these information will address the issues, but it also reduces utility since queries is no longer straightforward. Therefore, we propose a privacy-preserving scheme, PrivHome. It supports authentication, secure data storage and query for smart home systems. PrivHome provides data confidentiality as well as entity and data authentication to prevent an outsider from learning or modifying the data communicated between the devices, service provider, gateway, and the user. It further provides privacy-preserving queries in such a way that the service provider, and the gateway does not learn content of the data. To the best of our knowledge, privacy-preserving queries for smart home systems has not been considered before. Under our scheme is a new, lightweight entity and key-exchange protocol, and an efficient searchable encryption protocol. Our scheme is practical as both protocols are based solely on symmetric cryptographic techniques. We demonstrate efficiency and effectiveness of our scheme based on experimental and simulation results, as well as comparisons to existing smart home security protocols.

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“…Fu et al [Fu, Wu and Wang (2017)] improved accuracy by using center word expansion. Fu et al [Fu, Xia and Sun (2018)] proposed the conceptual hierarchical semantic search scheme under dual servers. Fig.…”

Section: Semantic Searchmentioning

confidence: 99%

Privacy-preserving Content-aware Search based on Two-level Index

Fu¹,

Xia²,

Liu³

et al. 2019

Computers, Materials &Amp; Continua

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Nowadays, cloud computing is used more and more widely, more and more people prefer to using cloud server to store data. So, how to encrypt the data efficiently is an important problem. The search efficiency of existed search schemes decreases as the index increases. For solving this problem, we build the two-level index. Simultaneously, for improving the semantic information, the central word expansion is combined. The purpose of privacy-preserving content-aware search by using the two-level index (CKESS) is that the first matching is performed by using the extended central words, then calculate the similarity between the trapdoor and the secondary index, finally return the results in turn. Through experiments and analysis, it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.

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Semantic-Aware Searching Over Encrypted Data for Cloud Computing

Cited by 72 publications

References 51 publications

FSSE: An Effective Fuzzy Semantic Searchable Encryption Scheme Over Encrypted Cloud Data

FSSE: An Effective Fuzzy Semantic Searchable Encryption Scheme Over Encrypted Cloud Data

PrivHome: Privacy-Preserving Authenticated Communication in Smart Home Environment

Privacy-preserving Content-aware Search based on Two-level Index

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