Efficiency and security in similarity cloud services

Kozák, Štěpán; Zezula, Pavel

doi:10.14778/2536274.2536334

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Indexing concept mentioned in (Kozak and Zezula, 2013), (Hong Lu et al, 2006), (Bin Cui et al, 2005), (Gil-Costa and Marin, 2012) play an important role in performing similarity search. (Kozak and Zezula, 2013) propose two new similarity indexes EM-Index and DSH Index that are apt for search systems outsourced in a cloud and also guarantee data privacy. EM-Index proves profitable by supporting precise evaluation of the range queries and efficient update operations while DSH guarantee higher privacy level.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…In last years, some of secure image retrieval systems exist in literature. Kozak [4] presented an evaluation of the existing secure indexing schemes; the evaluation criteria were about (usability, security, and efficiency) and suggested two new methods namely EM-Index and Dynamic Secure Hash-based Index. The balance of the first proposed technique is more on the efficiency side while the other (DSH Index) shifts the balance more to the privacy side.…”

Section: Introductionmentioning

confidence: 99%

Encrypted Image Retrieval System based on Features Analysis

Gaata

Hantoosh²

2018

MJS

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Content-based search provides an important tool for users to consume the ever-growing digital media repositories. However, since communication between digital products takes place in a public network, the necessity of security for digital images becomes vital. Hence, the design of secure content-based image retrieval system is becoming an increasingly demanding task as never before. In this paper, the secure CBIR with additional improvement for the image retrieval has been presented. The proposed system consists of six phases briefly described as follows: first, feature extraction phase, which produces the low-level quantitative description of the image (color and texture) that used in calculation of similarity score and image indexing. Second, indexing for search process phase, hash table and bloom filter were employed for classification. Third, feature encryption phase, where content protection is performed using a method developed by us (including Chaotic Logistic Map). Fourth, image encryption phase, the chaos and stream cipher systems were applied as an image encryption system in order to achieve image security. Fifth, the retrieval phase, which provides a group of images replying the query based on the similarity score between images, calculated using the extracted features from each image. Finally, Relevance feedback phase, a technique that attempts to capture the user's needs through iterative feedback. Although the system proved its efficiency in search performance (with 88% of average precision), security strength, and computational complexity, it does not mean the optimal system is designed, since some weakness points still can be found that are suggested to be improved as a future work.

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