Efficient request-based comparable encryption scheme based on sliding window method

Chen, Peng; Ye, Jun; Chen, Xiaofeng

doi:10.1007/s00500-015-1766-6

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Sliding window technology can bring in the reduction in the amount of computation and management overhead. Algorithm 1 illustrates details of sliding window method [32].…”

Section: Sliding Window Method Sliding Window Methods Proposed Bymentioning

confidence: 99%

“…Compute and store fl 2 ( ) ; (8) if ̸ = 0 then (9) Compute and store fl ⋅ ; (10) else (11) is a zero window. (12) Return ; Algorithm 1: The sliding window method [32]. to right in each layer with ⋅ , ⋅ , and ⋅ V defined as follows, where set Ω represents the set of left subtrees, set Ω represents the set of right subtrees, set Ω root represents the root of tree, and set Θ represents the set of leaf nodes.…”

Section: Position-aware Merkle Tree Merkle Hash Treementioning

confidence: 99%

“…Although this scheme can make comparable query operations over ciphertexts, it brings in high computational and storage burden. To this end, an efficient request-based comparable encryption scheme was discussed by Chen et al [32] through utilizing sliding window method to reduce computational and storage burden. To further relief ciphertexts storage space, SCE scheme was presented by Furukawa through using PPE idea [33].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparable Encryption Scheme over Encrypted Cloud Data in Internet of Everything

Meng

Chen

et al. 2017

Security and Communication Networks

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User authentication has been widely deployed to prevent unauthorized access in the new era of Internet of Everything (IOE). When user passes the legal authentication, he/she can do series of operations in database. We mainly concern issues of data security and comparable queries over ciphertexts in IOE. In traditional database, a Short Comparable Encryption (SCE) scheme has been widely used by authorized users to conduct comparable queries over ciphertexts, but existing SCE schemes still incur high storage and computational overhead as well as economic burden. In this paper, we first propose a basic Short Comparable Encryption scheme based on sliding window method (SCESW), which can significantly reduce computational and storage burden as well as enhance work efficiency. Unfortunately, as the cloud service provider is a semitrusted third party, public auditing mechanism needs to be furnished to protect data integrity. To further protect data integrity and reduce management overhead, we present an enhanced SCESW scheme based on position-aware Merkle tree, namely, PT-SCESW. Security analysis proves that PT-SCESW and SCESW schemes can guarantee completeness and weak indistinguishability in standard model. Performance evaluation indicates that PT-SCESW scheme is efficient and feasible in practical applications, especially for smarter and smaller computing devices in IOE.

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“…Sliding window technology can bring in the reduction in the amount of computation and management overhead. Algorithm 1 illustrates details of sliding window method [32].…”

Section: Sliding Window Method Sliding Window Methods Proposed Bymentioning

confidence: 99%

Section: Position-aware Merkle Tree Merkle Hash Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparable Encryption Scheme over Encrypted Cloud Data in Internet of Everything

Meng

Chen

et al. 2017

Security and Communication Networks

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“…The selective search algorithm mainly uses a hierarchical clustering method to segment and get the candidate target areas. A more detailed description about the selective search algorithm is listed in references [23][24][25]. The candidate areas obtained using the selective search algorithm are sent to a convolution neural network for classification to determine their type.…”

Section: Target Segmentationmentioning

confidence: 99%

A Multi-Target Detection Algorithm for Infrared Image based on Retinex and LeNet5 Neural Network

Yun¹

2018

IJPE

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Object detection in infrared video images is an important and challenging work. Due to low resolution, poor contrast, and low visual quality, target detection in infrared images is inefficient and prone to having higher false positive and lower precision rates. To improve detection efficiency, according to the characteristics of infrared images, we proposed a multi-target detection algorithm based on image enhancement and the LeNet5 deep neural network. In our method, we used the Retinex image enhance algorithm to protrude the edge contour and contrast, highlight the detailed features, and enhance the overall visibility of infrared images. In particular, the LeNet5 convolution neural network and CVC vehicle-assisted driving database were used to train the interesting target in the infrared image to generate the target data model, and the selective search algorithm was used to segment the candidate detect object regions in the image. The separated candidate regions were sent to the trained data model to classify the type and locate the position of objects in the image. The simulation results in CVC infrared image subset datasets show that our algorithm has higher detection speed and accuracy than the traditional HOG-based and LBP-based detection algorithms.

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“…Hence, they propose an efficient mutual verifiable PDP scheme, which utilizes Diffie–Hellman shared key to construct the homomorphic authenticator. The aforementioned methods are more efficient and secure regarding some exiting ones, such as .…”

Section: Introductionmentioning

confidence: 99%

Human continuous activity recognition based on energy-efficient schemes considering cloud security technology

Chen

Castiglione

et al. 2016

Security Comm. Networks

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Smartphone is broadly applicable to the human activity recognition (HAR) mobile devices. However, energy consumption becomes a big obstacle to such mobile devices of real‐time monitoring. In order to solve this problem, this paper presents a method of activity recognition based on energy‐efficient schemes. In terms of data acquisition and processing, energy‐efficient schemes adopt the best sample rate and extract the most effective feature combinations in accordance with the different activities, so as not to increase energy consumption; while in terms of recognition algorithm, we adopt the improved structure of multi‐class support vector machine, combine it with the probability of activity occurrence, so as to reduce the time complexity of recognition. This method can minimize energy consumption greatly under the premise of maintaining higher recognition accuracy. Moreover, this paper adopts mobile cloud security technology to reduce potential risk of the smartphone's data transmission and processing. We present our work with an experimental study, and our experiments show that the accuracy of activity recognition based on energy‐efficient schemes we proposed is up to 90.6%. In addition, this method will save 51.0% energy than that when sample rate and extracted features are, respectively, fixed at 100Hz and combined features. Copyright © 2016 John Wiley & Sons, Ltd.

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Efficient request-based comparable encryption scheme based on sliding window method

Cited by 11 publications

References 26 publications

Comparable Encryption Scheme over Encrypted Cloud Data in Internet of Everything

Comparable Encryption Scheme over Encrypted Cloud Data in Internet of Everything

A Multi-Target Detection Algorithm for Infrared Image based on Retinex and LeNet5 Neural Network

Human continuous activity recognition based on energy-efficient schemes considering cloud security technology

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