Secure outsourcing SIFT: Efficient and Privacy-Preserving Image Feature Extraction in the Encrypted Domain

Jiang, Linzhi; Xu, Chunxiang; Wang, Xiaofang; Luo, Bo; Wang, Huaqun

doi:10.1109/tdsc.2017.2751476

Cited by 31 publications

(10 citation statements)

References 25 publications

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“…Chang et al adopted pseudorandom functions to solve the problem of privacy preserving keyword searches on remote encrypted data and solved the update problem [17]. Thereafter, some researchers in the field of security proposed a lot of outspread searchable encryption solutions [18][19][20][21][22]. Wang et al investigate the problem of secure and efficient similarity search over outsourced cloud data and proposed a new symbol-based trietraverse searching mechanism [18].…”

Section: Related Workmentioning

confidence: 99%

“…The questions of the secure and efficient ranked search over encrypted outsourcing data were studied in the literatures [19,20]. Du et al presented a dynamic multiclient searchable symmetric encryption scheme supporting Boolean queries which allowed the data owner to authorize multiple users to excute Boolean queries over the encrypted data [21]. Li Wireless Communications and Mobile Computing techniques to present a dynamic searchable symmetric encryption scheme and addressed the key sharing problem [22], but all the searchable encryption solutions cannot be used to perform optimal route finding with support for semantic search over encrypted graph data.…”

Section: Related Workmentioning

confidence: 99%

“…The searchable encryption is a research hotspot in the area of information security, and the research progress in this field has also been advancing. Soon after, some dynamic and extended searchable encryption schemes have emerged [18][19][20][21][22], but the above searchable encryption schemes cannot be used to implement route finding with support for semantic search on encrypted graph. Recently, some researchers have studied and implemented some query schemes on the encrypted graph [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

Chen

et al. 2021

Wireless Communications and Mobile Computing

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The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

Chen

et al. 2021

Wireless Communications and Mobile Computing

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“…Since then, many secure SIFT feature extraction schemes are studied. To save storage space and enhance efficiency, Jiang et al proposed an efficient privacypreserving SIFT feature extraction method [17]. In this method, the authors designed an encoding method which converts a non-integer into an integer.…”

Section: Related Workmentioning

confidence: 99%

“…There is an increasing need for secure image data sharing in cloud computing. Numerous previous works focused on secure image feature extraction from encrypted images such as speeded up robust feature (SURF) [14], scale-invariant feature transform (SIFT) [15][16][17][18], the histogram of oriented gradient (HOG) [19] and local binary pattern (LBP) [20][21][22]. To save storage space and communication bandwidth, digital images are often compressed before uploading on the cloud server.…”

Section: Introductionmentioning

confidence: 99%

Secure Outsourcing Algorithm of BTC Feature Extraction in Cloud Computing

Jiang

Yang

2020

IEEE Access

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Advances in cloud computing have aroused many researchers' interest in privacy-preserving feature extraction over outsourced multimedia data, especially private image data. Since block truncation coding (BTC) is known as a simple and efficient technology for image compression, this paper focuses on privacy-preserving feature extraction in BTC compressed domain. We propose a privacy-preserving computation of BTC feature extraction over massive encrypted images (also called PPBTC). First, all images are uploaded to the cloud after encryption. The privacy-preserving image encryption process consists of block permutation, pixel diffusion, and a bit-plane random shift. BTC features remain unchanged after encryption and the cloud server can directly extract BTC features from the encrypted images. Some analyses and experimental results demonstrate that the proposed privacy-preserving feature extraction scheme for BTCcompressed images is efficient and secure, and it can be applied to secure image computation applications in cloud computing. INDEX TERMS Feature extraction, privacy-preserving, block truncation coding, homomorphic encryption.

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Privacy-Preserving Edge Video Analytics

2020

Fog/Edge Computing for Security, Privacy, and Applications

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Secure outsourcing SIFT: Efficient and Privacy-Preserving Image Feature Extraction in the Encrypted Domain

Cited by 31 publications

References 25 publications

Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

Secure Outsourcing Algorithm of BTC Feature Extraction in Cloud Computing

Privacy-Preserving Edge Video Analytics

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