Split keyword fuzzy and synonym search over encrypted cloud data

Raghavendra, Srinivas; Girish, S.; Geeta, C M; Buyya, Rajkumar; Venugopal, K. R.; Iyengar, S. Sitharama; Patnaik, L. M.

doi:10.1007/s11042-017-5055-5

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…So the scheme ensures that content other than access and search requests are not compromised. This section will prove that the fuzzy search scheme designed in this paper is in line with the non-adaptive semantic security requirements [Raghavendra, Girish and Geeta (2018)]. The non-adaptive attack model only considers adversaries (e.g., cloud servers), who cannot select trapdoor-based search requests and previous search results, because only users with authorized private keys can generate search traps [Wang, Chen, Li et al (2017); Shen, Wang, Li et al (2018)].…”

Section: Security Analysismentioning

confidence: 82%

Fuzzy Search for Multiple Chinese Keywords in Cloud Environment

Fang¹,

Wang²,

Wang³

et al. 2019

Computers, Materials &Amp; Continua

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With the continuous development of cloud computing and big data technology, the use of cloud storage is more and more extensive, and a large amount of data is outsourced for public cloud servers, and the security problems that follow are gradually emerging. It can not only protect the data privacy of users, but also realize efficient retrieval and use of data, which is an urgent problem for cloud storage. Based on the existing fuzzy search and encrypted data fuzzy search schemes, this paper uses the characteristics of fuzzy sounds and polysemy that are unique to Chinese, and realizes the synonym construction of keywords through Chinese Pinyin and Chinese-English translation, and establishes the fuzzy word and synonym set of keywords. This paper proposes a Chinese multi-keyword fuzzy search scheme in a cloud environment, which realizes the fuzzy search of multiple Chinese keywords and protects the private key by using a pseudo-random function. Finally, the safety analysis and system experiments verify that the scheme has high security, good practicability, and high search success rate.

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Section: Security Analysismentioning

confidence: 82%

Fuzzy Search for Multiple Chinese Keywords in Cloud Environment

Fang¹,

Wang²,

Wang³

et al. 2019

Computers, Materials &Amp; Continua

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“…The relevance score is computed in the client side, so sensitive information of the data owner can be access by the unauthorized users. In [14] an efficient search result approach is proposed based on the split keyword fuzzy and synonym search. The wildcard technique is used in this process to store the keyword in index safely.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, simulation results obtained from the implementation is compared with the existing method discussed in papers RGMTS [11], IVCKSE [12] and SKFS [14]. Three performance metrics are considered to compare the proposed work with existing work.…”

Section: B Comparative Analysismentioning

confidence: 99%

Secure Ranked Multi-Keyword Search based on Modified Blowfish Algorithm and AVL Tree in Untrusted Cloud Environment

Swami,

Das

2019

IJEAT

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Advancement in cloud services growing day by day motivates a huge number of data proprietors to store their valuable data in cloud servers. For privacy issue, datasets that contain sensitive information are encrypted first and then outsourced into the cloud server. In this paper, ranked multi keyword search based on AVL tree, especially for several data owners, has been proposed. To avoid unauthorized access, the proposed work encrypts the data using a Modified Blowfish (MB) algorithm before outsourcing. MB algorithm introduces # operation instead of normal OR operation in conventional blowfish algorithm. This MB algorithm provides robustness against any intruding whereas the conventional blowfish algorithm insecure for many applications. To achieve a proficient search, every data owner’s index based on AVL tree is encrypted by way of additive order and the privacy-preserving family is formed. The cloud server is then permitted to combine these indexes effectually without knowing the index content. An Iterative Deepening Depth First Search (IDDFS) procedure is used to discover the matching file for the data user request. Finally, our proposed work provides secure data outsourcing to cloud server compared to the other existing methods. The proposed work considers three metrics for the performance evaluation process, viz. precision, index construction time and search time.

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“…The concept of similarity search (Zezula et al, 2006) (Raghavendra et al, 2015) is applicable to a wide range of data and infinite number of various similarity functions. The time series pattern which has been collected in hourly or weekly basis can be searched by the scientist for similar patterns to indicate an interesting phenomenon.…”

Section: Introductionmentioning

confidence: 99%

RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment

Raghavendra

Nithyashree

Geeta

et al. 2016

International Journal of Organizational and Collective Intelligence

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This paper involves a cloud computing environment in which the dataowner outsource the similarity search service to a third party service provider. Privacy of the outsourced data is important because they may be confidential data. The data should be made available to the authorized client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called RSSMSO which has build phase, query phase, data transformation and search phase. The build phase and the query phase are about uploading the data and querying the data respectively; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. The RSSMSO technique provides enhanced query accuracy with low communication cost. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a low cost in comparison with FDH ( ) covering k objects from X with the smallest distances to given q M ∈ (Kozak, Novak and Zezula, 2012). Motivation:Existing solutions offer any one of the following, its either query efficiency and no privacy, or complete data privacy and less query efficiency. Metric Preserving Transformation(MPT) and Flexible Distance-based Hashing(FDH) are existing methods which shifts search functionality to the server. The MPT stores relative distance information at the server with respect to a private set of anchor objects and guarantees to fetch exact results, but it needs two rounds of communication. The FDH method takes a single round of communication, but does not guarantee to retrieve the exact result. Hence our objective is to retrieve the exact result in just a single round of communication. Contribution: In this paper, we describe a new technique for similarity search on metric data named as Rapid Similarity Search on Metric Space Object (RSSMSO). RSSMSO supports for fast retrieval of resultant object with accuracy and it provides privacy for objects by using data transformation steps before uploading to the cloud server. We suggest new technique to overcome the drawbacks of the outsourced similarity search on metric data assets (Yiu et al., 2012). The implication of the contributions are:1. RSSMSO method is developed to retrive Fast similarity search on metric space data. It helps to reduces communication cost over huge cloud data. 2. RSSMSO algorithm reduces the communication cost and increases accuracy. 3. Flexible Distance-based hashing methods allow the client to specify the theta ( θ ) value for increasing the accuracy of the result. Theta value would change depending on the size of the data set. In RSSMSO algorithm, θ value would always be 1, even when data set size vary. Hence we are able to retrieve the exact result with a very low theta value in a single communication round.

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Split keyword fuzzy and synonym search over encrypted cloud data

Cited by 8 publications

References 29 publications

Fuzzy Search for Multiple Chinese Keywords in Cloud Environment

Fuzzy Search for Multiple Chinese Keywords in Cloud Environment

Secure Ranked Multi-Keyword Search based on Modified Blowfish Algorithm and AVL Tree in Untrusted Cloud Environment

RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment

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