A Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation

Xia, Zhihua; Zhu, Yi; Sun, Xingming; Wang, Jin

doi:10.14257/ijfgcn.2013.6.6.08

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…[22] proposed an efficient privacy preserving cloud based secure image feature extraction and comparison technique. Similar construction for ranked image retrieval is proposed by [29,17,23]. These systems depend on highly capable cloud server for preforming image similarity query.…”

Section: Related Workmentioning

confidence: 99%

A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data

Shaon

Kantarcıoğlu

2016

Data and Applications Security and Privacy XXX

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Over the last few years, data storage in cloud based services has been very popular due to easy management and monetary advantages of cloud computing. Recent developments showed that such data could be leaked due to various attacks. To address some of these attacks, encrypting sensitive data before sending to cloud emerged as an important protection mechanism. If the data is encrypted with traditional techniques, selective retrieval of encrypted data becomes challenging. To address this challenge, efficient searchable encryption schemes have been developed over the years. Almost all of the existing searchable encryption schemes are developed for keyword searches and require running some code on the cloud servers. However, many of the existing cloud storage services (e.g., Dropbox 1 , Box 2 , Google Drive 3 , etc.) only allow simple data object retrieval and do not provide computational support needed to realize most of the searchable encryption schemes. In this paper, we address the problem of efficient execution of complex search queries over wide range of encrypted data types (e.g., image files) without requiring customized computational support from the cloud servers. To this end, we provide an extensible framework for supporting complex search queries over encrypted multimedia data. Before any data is uploaded to the cloud, important features are extracted to support different query types (e.g., extracting facial features to support face recognition queries) and complex queries are converted to series of object retrieval tasks for cloud service. Our results show that this framework may support wide range of image retrieval queries on encrypted data with little overhead and without any change to underlying data storage services.

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Section: Related Workmentioning

confidence: 99%

A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data

Shaon

Kantarcıoğlu

2016

Data and Applications Security and Privacy XXX

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“…Similarity search concept is considered in the following references (Xia et al, 2013), (Tsymbal et al, 2014), (Popivanov and Miller, 2002). (Xia et al, 2013) performs similarity search on encrypted images based on a secure transformation method. The transformation used does not mortify the result accuracy and also keeps the confidentiality of the data intact.…”

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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“…Moreover, all database update operation and query authorization relies on the database owner i.e., a single point of failure. Xia et al, [9] proposed a scheme for basic similarity search over encrypted images based on a secure transformation method that protected the information about features, and did not degrade the result accuracy. The proposed scheme protected the confidentiality of image database, feature vectors, and user's query.…”

Section: Contributionmentioning

confidence: 99%

DRSIG: Domain and Range Specific Index Generation for encrypted Cloud data

Raghavendra

Mara

Buyya

et al. 2016

2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT)

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Abstract-One of the most fundamental services of cloud computing is Cloud storage service. Huge amount of sensitive data is stored in the cloud for easy remote access and to reduce the cost of storage. The confidential data is encrypt before uploading to the cloud server in order to maintain privacy and security. All conventional searchable symmetric encryption(SSE) schemes enable the users to search on the entire index file. In this paper, we propose the Domain and Range Specific Index Generation(DRSIG) scheme that minimizes the Index Generation time. This scheme adopts collection sort technique to split the index file into D Domains and R Ranges. The Domain is based on the length of the keyword; the Range splits within the domain based on the first letter of the keyword. A mathematical model is used to encrypt the indexed keyword that eliminates the information leakage. The time complexity of the index generation is O (NT × 3) where NT -Number of rows in index document and 3 is Number of columns in index document. Experiments have been conducted on real world dataset to validate proposed DRSIG scheme.It is observed that DRSIG scheme is efficient and provide more secure data than Ranked Searchable Symmetric Encryption(RSSE) Scheme.

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A Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation

Abstract: Abstract

Cited by 14 publications

References 22 publications

A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data

A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data

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

DRSIG: Domain and Range Specific Index Generation for encrypted Cloud data

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