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Journal articles on the topic 'Secure Outsourced Computation'

Author: Grafiati

Published: 6 September 2023

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1

Olakanmi, Oladayo Olufemi, and Adedamola Dada. "An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms." International Journal of Cloud Applications and Computing 9, no. 2 (2019): 79–98. http://dx.doi.org/10.4018/ijcac.2019040105.

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In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computat

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2

Blanton, Marina, and Mehrdad Aliasgari. "Secure outsourced computation of iris matching." Journal of Computer Security 20, no. 2-3 (2012): 259–305. http://dx.doi.org/10.3233/jcs-2012-0447.

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Sun, Yi, Qiaoyan Wen, Yudong Zhang, Hua Zhang, Zhengping Jin, and Wenmin Li. "Two-Cloud-Servers-Assisted Secure Outsourcing Multiparty Computation." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/413265.

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We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users’ public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively pr

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Shao, Jun, and Guiyi Wei. "Secure Outsourced Computation in Connected Vehicular Cloud Computing." IEEE Network 32, no. 3 (2018): 36–41. http://dx.doi.org/10.1109/mnet.2018.1700345.

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Treiber, Amos, Andreas Nautsch, Jascha Kolberg, Thomas Schneider, and Christoph Busch. "Privacy-preserving PLDA speaker verification using outsourced secure computation." Speech Communication 114 (November 2019): 60–71. http://dx.doi.org/10.1016/j.specom.2019.09.004.

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6

Yang, Yang, Xindi Huang, Ximeng Liu, et al. "A Comprehensive Survey on Secure Outsourced Computation and Its Applications." IEEE Access 7 (2019): 159426–65. http://dx.doi.org/10.1109/access.2019.2949782.

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Hong, Jun, Tao Wen, Quan Guo, and Zhengwang Ye. "Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/8109730.

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As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP) to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate

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Yang, Guangcan, Jiayang Li, Yunhua He, et al. "A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services." Applied Sciences 12, no. 16 (2022): 8126. http://dx.doi.org/10.3390/app12168126.

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Location-based services (LBSs) facilitate people’s lives; location-based service providers (LBSPs) usually outsource services to third parties to provide better services. However, the third party is a dishonest entity that might return incorrect or incomplete query results under the consideration of saving storage space and computation resources. In this paper, we propose a security-enhanced query result verification scheme (SEQRVS) for the outsourced data in a LBS. Specifically, while retaining fine-grained query result verification, we improve the construction process of verification objects

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Zhu, Youwen, Xingxin Li, Jian Wang, Yining Liu, and Zhiguo Qu. "Practical Secure Naïve Bayesian Classification Over Encrypted Big Data in Cloud." International Journal of Foundations of Computer Science 28, no. 06 (2017): 683–703. http://dx.doi.org/10.1142/s0129054117400135.

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Cloud can provide much convenience for big data storage and analysis. To enjoy the advantage of cloud service with privacy preservation, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted large-scale dataset in cloud, and propose a practical and secure scheme for the challenging problem. In our scheme, all the c

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Song, Mingyang, and Yingpeng Sang. "Secure Outsourcing of Matrix Determinant Computation under the Malicious Cloud." Sensors 21, no. 20 (2021): 6821. http://dx.doi.org/10.3390/s21206821.

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Computing the determinant of large matrix is a time-consuming task, which is appearing more and more widely in science and engineering problems in the era of big data. Fortunately, cloud computing can provide large storage and computation resources, and thus, act as an ideal platform to complete computation outsourced from resource-constrained devices. However, cloud computing also causes security issues. For example, the curious cloud may spy on user privacy through outsourced data. The malicious cloud violating computing scripts, as well as cloud hardware failure, will lead to incorrect resu

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Zong, Haoran, Hai Huang, and Shufang Wang. "Secure Outsourced Computation of Matrix Determinant Based on Fully Homomorphic Encryption." IEEE Access 9 (2021): 22651–61. http://dx.doi.org/10.1109/access.2021.3056476.

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12

Aloufi, Asma, Peizhao Hu, Yongsoo Song, and Kristin Lauter. "Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: A Survey." ACM Computing Surveys 54, no. 9 (2022): 1–37. http://dx.doi.org/10.1145/3477139.

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With capability of performing computations on encrypted data without needing the secret key, homomorphic encryption (HE) is a promising cryptographic technique that makes outsourced computations secure and privacy-preserving. A decade after Gentry’s breakthrough discovery of how we might support arbitrary computations on encrypted data, many studies followed and improved various aspects of HE, such as faster bootstrapping and ciphertext packing. However, the topic of how to support secure computations on ciphertexts encrypted under multiple keys does not receive enough attention. This capabili

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Zou, Ying, Zhen Zhao, Sha Shi, et al. "Highly Secure Privacy-Preserving Outsourced k-Means Clustering under Multiple Keys in Cloud Computing." Security and Communication Networks 2020 (March 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/1238505.

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Data clustering is the unsupervised classification of data records into groups. As one of the steps in data analysis, it has been widely researched and applied in practical life, such as pattern recognition, image processing, information retrieval, geography, and marketing. In addition, the rapid increase of data volume in recent years poses a huge challenge for resource-constrained data owners to perform computation on their data. This leads to a trend that users authorize the cloud to perform computation on stored data, such as keyword search, equality test, and outsourced data clustering. I

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Wei, Weiming, Chunming Tang, and Yucheng Chen. "Efficient Privacy-Preserving K-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation." Entropy 24, no. 8 (2022): 1145. http://dx.doi.org/10.3390/e24081145.

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Privacy-preserving machine learning has become an important study at present due to privacy policies. However, the efficiency gap between the plain-text algorithm and its privacy-preserving version still exists. In this paper, we focus on designing a novel secret-sharing-based K-means clustering algorithm. Particularly, we present an efficient privacy-preserving K-means clustering algorithm based on replicated secret sharing with honest-majority in the semi-honest model. More concretely, the clustering task is outsourced to three semi-honest computing servers. Theoretically, the proposed priva

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Sun, Xiaoqiang, Zhiwei Sun, Ting Wang, Jie Feng, Jiakai Wei, and Guangwu Hu. "A Privacy-Preserving Reinforcement Learning Approach for Dynamic Treatment Regimes on Health Data." Wireless Communications and Mobile Computing 2021 (November 23, 2021): 1–16. http://dx.doi.org/10.1155/2021/8952219.

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Based on the clinical states of the patient, dynamic treatment regime technology can provide various therapeutic methods, which is helpful for medical treatment policymaking. Reinforcement learning is an important approach for developing this technology. In order to implement the reinforcement learning algorithm efficiently, the computation of health data is usually outsourced to the untrustworthy cloud server. However, it may leak, falsify, or delete private health data. Encryption is a common method for solving this problem. But the cloud server is difficult to calculate encrypted health dat

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Sookhak, Mehdi, Adnan Akhunzada, Abdullah Gani, Muhammad Khurram Khan, and Nor Badrul Anuar. "Towards Dynamic Remote Data Auditing in Computational Clouds." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/269357.

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Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these technique

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Fan, Kai, Tingting Liu, Kuan Zhang, Hui Li, and Yintang Yang. "A secure and efficient outsourced computation on data sharing scheme for privacy computing." Journal of Parallel and Distributed Computing 135 (January 2020): 169–76. http://dx.doi.org/10.1016/j.jpdc.2019.09.008.

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Wang, Qihua, Huaqun Wang, Yufeng Wang, and Rui Guo. "A Distributed Access Control with Outsourced Computation in Fog Computing." Security and Communication Networks 2019 (July 8, 2019): 1–10. http://dx.doi.org/10.1155/2019/6782753.

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With the rapid development of information technology and the Internet of Things Technology (IOT), data security and healthy privacy are getting a lot of attention. In order to store, access, and share electronic health records, storage of this data is transferred to a third-party-cloud server. The security and privacy of electronic health records stored at date center or cloud server are not guaranteed. Before being sent to date center or cloud server, this data should be encrypted. Designing an efficient and secure fine-grained access control strategy for personal health records is facing eno

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Wang, Huawei, Ye Li, Yingnan Jiao, and Zhengping Jin. "An efficient outsourced attribute-based encryption scheme in the device-to-device mobile network." International Journal of Distributed Sensor Networks 15, no. 7 (2019): 155014771986550. http://dx.doi.org/10.1177/1550147719865507.

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Device-to-device communication is considered as one of the hopeful technologies for proximal communication, which plays a vital role in the wireless systems and 5G cellular networks. The outsourced attribute-based encryption scheme is convinced to be very suitable for secure device-to-device communication since it allows not only fine-grained sharing of encrypted data but also achieves high efficiency in the decryption of general attribute-based encryption schemes. However, almost all existing outsourced attribute-based encryption schemes can hardly be applied directly in the device-to-device

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Masood, Raziqa, Nitin Pandey, and Q. P. Rana. "Towards an efficient and secure computation over outsourced encrypted data using distributed hash table." International Journal of Cloud Computing 11, no. 3 (2022): 217. http://dx.doi.org/10.1504/ijcc.2022.124152.

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Pandey, Nitin, Q. P. Rana, and Raziqa Masood. "Towards an efficient and secure computation over outsourced encrypted data using distributed hash table." International Journal of Cloud Computing 11, no. 3 (2022): 217. http://dx.doi.org/10.1504/ijcc.2022.10048901.

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22

Banu, Dr Sameena, and Bibi Hajra Umm E Hani. "Mobile Finger Print Verification and Automatic Log in Platform Using Blockchain." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 737–41. http://dx.doi.org/10.22214/ijraset.2022.47256.

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Abstract: Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which however brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric data is encrypted and outsourced using Block Chain. To execute a biometric identification, the database owner

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Babenko, Mikhail, Andrei Tchernykh, Bernardo Pulido-Gaytan, et al. "Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control." Mathematics 10, no. 12 (2022): 2006. http://dx.doi.org/10.3390/math10122006.

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Homomorphic encryption with the ability to compute over encrypted data without access to the secret key provides benefits for the secure and powerful computation, storage, and communication of resources in the cloud. One of its important applications is fast-growing robot control systems for building lightweight, low-cost, smarter robots with intelligent brains consisting of data centers, knowledge bases, task planners, deep learning, information processing, environment models, communication support, synchronous map construction and positioning, etc. It enables robots to be endowed with secure

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Kumar, Dilip, Manoj Kumar, and Gaurav Gupta. "An Outsourced Decryption ABE Model using ECC in Internet of Things." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 06 (2021): 949–64. http://dx.doi.org/10.1142/s0218488521500422.

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Internet of Things (IoT) is a modern technology that is applicable almost everywhere nowadays. Everything is connected to the Internet in the modern digital era. IoT is a collection of things that are interconnected to share information. Devices connected to IoT networks have some limitations in performing heavy computational tasks because of the availability of less computational and battery power. Attribute Based Encryption (ABE) is a modern public-key cryptographic technique that provides privacy with access control. The bilinear map is an expensive operation that is used in most of the ABE

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Hou, Gaopan, Jianfeng Ma, Jiayi Li, and Chen Liang. "Audit Outsourced Data in Internet of Things." Security and Communication Networks 2021 (May 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/6662135.

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With the increase in network transmission rates, the Internet of Things (IoT) has gradually become a trend. Users can upload the data generated by the device to the cloud database to save local storage space, thereby reducing local storage costs. Because uploading data to the cloud loses physical control of the data, an audit is required. Traditional audit protocols are not completely suitable for lightweight devices in the IoT. This paper proposes a new type of audit protocol suitable for lightweight devices with weak computing power. This protocol transfers part of the computation of data ta

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Yang, Changsong, Yueling Liu, and Xiaoling Tao. "Assure deletion supporting dynamic insertion for outsourced data in cloud computing." International Journal of Distributed Sensor Networks 16, no. 9 (2020): 155014772095829. http://dx.doi.org/10.1177/1550147720958294.

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With the rapid development of cloud computing, an increasing number of data owners are willing to employ cloud storage service. In cloud storage, the resource-constraint data owners can outsource their large-scale data to the remote cloud server, by which they can greatly reduce local storage overhead and computation cost. Despite plenty of attractive advantages, cloud storage inevitably suffers from some new security challenges due to the separation of outsourced data ownership and its management, such as secure data insertion and deletion. The cloud server may maliciously reserve some data c

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Zhao, Ruoli, Yong Xie, Xingxing Jia, Hongyuan Wang, and Neeraj Kumar. "Practical Privacy Preserving-Aided Disease Diagnosis with Multiclass SVM in an Outsourced Environment." Security and Communication Networks 2022 (October 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/7751845.

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With the rapid development of cloud computing and machine learning, using outsourced stored data and machine learning model for training and online-aided disease diagnosis has a great application prospect. However, training and diagnosis in an outsourced environment will cause serious challenges to the privacy of data. At present, many scholars have proposed privacy preserving machine learning schemes and made a lot of progress, but there are still great challenges in security and low client load. In this paper, we propose a complete privacy preserving outsourced multiclass SVM training and ai

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Deng, Guoqiang, Min Tang, Yuhao Zhang, Ying Huang, and Xuefeng Duan. "Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification." Applied Sciences 12, no. 24 (2022): 12873. http://dx.doi.org/10.3390/app122412873.

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Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a

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Zhao, Shengnan, Xiangfu Song, Han Jiang, Ming Ma, Zhihua Zheng, and Qiuliang Xu. "An Efficient Outsourced Oblivious Transfer Extension Protocol and Its Applications." Security and Communication Networks 2020 (December 5, 2020): 1–12. http://dx.doi.org/10.1155/2020/8847487.

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Oblivious transfer (OT) is a cryptographic primitive originally used to transfer a collection of messages from the sender to the receiver in an oblivious manner. OT extension protocol reduces expensive asymmetric operations by running a small number of OT instances first and then cheap symmetric operations. While most earlier works discussed security model or communication and computation complexity of OT in general case, we focus on concrete application scenarios, especially where the sender in the OT protocol is a database with less computation and limited interaction capability. In this pap

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Wu, Yiqi, Fazhi He, and Yueting Yang. "A Grid-Based Secure Product Data Exchange for Cloud-Based Collaborative Design." International Journal of Cooperative Information Systems 29, no. 01n02 (2020): 2040006. http://dx.doi.org/10.1142/s0218843020400067.

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As a new design and manufacture paradigm, Cloud-Based Collaborative Design (CBCD) has motivated designers to outsource their product data and design computation onto the cloud service. Despite non-negligible benefits of CBCD, there are potential security threats for the outsourced product data, such as intellectual property, design intentions and private identity, which has become an interest point. This paper presents a novel secure product data exchange (PDE) in the processes of CBCD. Different from general cloud security mechanism, our method is content-based. We first show an outline of th

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Cao, Fang, Jiayi Sun, Xiangyang Luo, Chuan Qin, and Ching-Chun Chang. "Privacy-preserving inpainting for outsourced image." International Journal of Distributed Sensor Networks 17, no. 11 (2021): 155014772110590. http://dx.doi.org/10.1177/15501477211059092.

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In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to

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Hyder, M. F., S. Tooba, and Waseemullah. "Performance Evaluation of RSA-based Secure Cloud Storage Protocol using OpenStack." Engineering, Technology & Applied Science Research 11, no. 4 (2021): 7321–25. http://dx.doi.org/10.48084/etasr.4220.

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In this paper, the implementation of the General Secure Cloud Storage Protocol is carried out and instantiated by a multiplicatively Homomorphic Encryption Scheme (HES). The protocol provides a system for secure storage of data over the cloud, thereby allowing the client to carry out the operational tasks on it efficiently. The work focuses on the execution of five major modules of the protocol. We also evaluate the performance of the protocol with respect to the computation cost of these modules on the basis of different security parameters and datasets by conducting a series of experiments.

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Li, Jiguo, Fengjie Sha, Yichen Zhang, Xinyi Huang, and Jian Shen. "Verifiable Outsourced Decryption of Attribute-Based Encryption with Constant Ciphertext Length." Security and Communication Networks 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3596205.

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Outsourced decryption ABE system largely reduces the computation cost for users who intend to access the encrypted files stored in cloud. However, the correctness of the transformation ciphertext cannot be guaranteed because the user does not have the original ciphertext. Lai et al. provided an ABE scheme with verifiable outsourced decryption which helps the user to check whether the transformation done by the cloud is correct. In order to improve the computation performance and reduce communication overhead, we propose a new verifiable outsourcing scheme with constant ciphertext length. To be

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Chen, Jiannan, Ying Wang, Zhaohui Huang, Conghao Ruan, and Chunqiang Hu. "A Decentralized Public Auditing Scheme for Secure Cloud Storage Based on Blockchain." Wireless Communications and Mobile Computing 2022 (October 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/3688164.

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The cloud storage service has brought great convenience to the customer, which can save massive storage and computation resources via outsourcing the data to cloud service provider (CSP). However, the security issues are the biggest challenge such as data integrity. The user can verify the integrity of outsourced data through a remote data auditing solution without retrieving original data from cloud, however, the auditing procedure has heavy computational overhead, which employs third party auditor (TPA) to conduct auditing task on behalf of users. In this paper, we propose a decentralized pu

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Zhou, Fucai, Su Peng, Jian Xu, and Zifeng Xu. "Identity-Based Batch Provable Data Possession with Detailed Analyses." International Journal of Foundations of Computer Science 28, no. 06 (2017): 743–60. http://dx.doi.org/10.1142/s0129054117400160.

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In the era of big data, cloud storages become prevalent while security issues on outsourced data emerge. Without the physical control of the data owners, the outsourced data faces more risks. Provable Data Possession (PDP) is a probabilistic technique for checking whether data is correctly stored in remote servers without retrieving the entire data. For many previous PDP schemes, correctly choosing public keys for data owners relies on the security of Public Key Infrastructure (PKI), which is resource consuming. In this paper, we propose an Identity-Based Batch Provable Data Possession (ID-BPD

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Kotte, Bhuvaneswari, and T. Sirisha Madhuri. "Providing Security to Ensure Biometric Identification System in Cloud." Asian Journal of Computer Science and Technology 8, no. 3 (2019): 1–5. http://dx.doi.org/10.51983/ajcst-2019.8.3.2731.

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Biometric identification has rapidly growing in recent years. With the development of cloud computing, database owners are incentivized to outsource the bulk size of biometric data and identification tasks to the cloud to liberate the costly storage and computation costs, which however brings potential attacks to users’ privacy. In this paper, we propose an adequate and security to keep biometric identification outsourcing scheme. Categorically, the biometric data is encrypted and outsourced to the cloud server. To get a biometric identification, the database owner encrypts the query data and

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Guo, Rui, Chaoyuan Zhuang, Huixian Shi, Yinghui Zhang, and Dong Zheng. "A lightweight verifiable outsourced decryption of attribute-based encryption scheme for blockchain-enabled wireless body area network in fog computing." International Journal of Distributed Sensor Networks 16, no. 2 (2020): 155014772090679. http://dx.doi.org/10.1177/1550147720906796.

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Wireless body area network includes some tiny wearable sensors for monitoring the physiological data of user, which has been a promising method of promoting the quality and efficiency greatly in healthcare. The collected physical signs are aggregated into the medical documents and uploaded to cloud server for utilizing by the remote user. As these files are highly sensitive privacy data, there is a vital challenge that constructs a secure and efficient communication architecture in this application scenario. Based on this standpoint, we present a lightweight verifiability ciphertext-policy att

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Kim, Hyeong-Jin, Hyunjo Lee, Yong-Ki Kim, and Jae-Woo Chang. "Privacy-preserving kNN query processing algorithms via secure two-party computation over encrypted database in cloud computing." Journal of Supercomputing 78, no. 7 (2022): 9245–84. http://dx.doi.org/10.1007/s11227-021-04286-2.

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AbstractSince studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving kNN query processing algorithms have been proposed over the encrypted database. However, the existing algorithms are either insecure or inefficient. Therefore, in this paper we propose a privacy-preserving kNN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hiding data acces

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Ma, Haobin, Dehua Zhou, Peng Li, and Xiaoming Wang. "EVOAC-HP: An Efficient and Verifiable Outsourced Access Control Scheme with Hidden Policy." Sensors 23, no. 9 (2023): 4384. http://dx.doi.org/10.3390/s23094384.

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As medical data become increasingly important in healthcare, it is crucial to have proper access control mechanisms, ensuring that sensitive data are only accessible to authorized users while maintaining privacy and security. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an attractive access control solution that can offer effective, fine-grained and secure medical data sharing, but it has two major drawbacks: Firstly, decryption is computationally expensive for resource-limited data users, especially when the access policy has many attributes, limiting its use in large-scale data-s

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Peng, Ningduo, Guangchun Luo, Ke Qin, and Aiguo Chen. "Query-Biased Preview over Outsourced and Encrypted Data." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/860621.

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For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from t

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Wang, Jiafan, and Sherman S. M. Chow. "Forward and Backward-Secure Range-Searchable Symmetric Encryption." Proceedings on Privacy Enhancing Technologies 2022, no. 1 (2021): 28–48. http://dx.doi.org/10.2478/popets-2022-0003.

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Abstract Dynamic searchable symmetric encryption (DSSE) allows a client to query or update an outsourced encrypted database. Range queries are commonly needed. Previous range-searchable schemes either do not support updates natively (SIGMOD’16) or use file indexes of many long bit-vectors for distinct keywords, which only support toggling updates via homomorphically flipping the presence bit. (ESORICS’18). We propose a generic upgrade of any (inverted-index) DSSE to support range queries (a.k.a. range DSSE), without homomorphic encryption, and a specific instantiation with a new trade-off redu

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Hong, Linjian, Kai Zhang, Junqing Gong, and Haifeng Qian. "A Practical and Efficient Blockchain-Assisted Attribute-Based Encryption Scheme for Access Control and Data Sharing." Security and Communication Networks 2022 (September 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/4978802.

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Attribute-based encryption (ABE) is a powerful encryption scheme with flexible access control over encrypted data that has been widely adopted in cloud computing scenarios to facilitate data sharing. However, despite convenience and efficiency provided by data sharing based on cloud, it is commonly vulnerable to issues like key abuse (namely, illegal key sharing by user or key distribution by authority) and key escrow (namely, illegal decryption by ABE authority). Hence, exploring a more secure ABE scheme that can be key abuse and key escrow resistant is crucial. Furthermore, data modification

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Zhou, Xingguang, Jianwei Liu, Zongyang Zhang, and Qianhong Wu. "Secure Outsourced Medical Data against Unexpected Leakage with Flexible Access Control in a Cloud Storage System." Security and Communication Networks 2020 (February 10, 2020): 1–20. http://dx.doi.org/10.1155/2020/8347213.

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The application of cloud storage system has been deployed widely in recent years. A lot of electronic medical records (EMRs) are collected and uploaded to the cloud for scalable sharing among the authority users. It is necessary to guarantee the confidentiality of EMRs and the privacy of EMR owners. To achieve this target, we summarize a series of attack behaviors in the cloud storage system and present the security model against many types of unexpected privacy leakage. Privacy of unassailed EMRs is guaranteed in this model, and the influence of privacy leakage is controlled in a certain scop

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Zhou, Jun, Zhenfu Cao, Zhan Qin, Xiaolei Dong, and Kui Ren. "LPPA: Lightweight Privacy-Preserving Authentication From Efficient Multi-Key Secure Outsourced Computation for Location-Based Services in VANETs." IEEE Transactions on Information Forensics and Security 15 (2020): 420–34. http://dx.doi.org/10.1109/tifs.2019.2923156.

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Zhen, Yan, Yilan Chui, Puning Zhang, and Huan Liu. "Multiauthority Attribute-Based Keyword Search over Cloud-Edge-End Collaboration in IoV." Wireless Communications and Mobile Computing 2022 (May 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/3513063.

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The development of cloud computing and edge computing makes it possible to store and share Internet of Vehicles (IoV) data on a large scale, which greatly contributes to traffic intelligence, but outsourced data confidentiality and user privacy cannot be guaranteed. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) scheme can achieve both fine-grained access control and secure data sharing. However, existing CP-ABE schemes owns high computational complexity, and the adopted single attribute authority mode is burdensome to resource-limited IoV. Thus, this paper proposes a Multiauthority

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Kaushik, Shweta, and Charu Gandhi. "Capability Based Outsourced Data Access Control with Assured File Deletion and Efficient Revocation with Trust Factor in Cloud Computing." International Journal of Cloud Applications and Computing 10, no. 1 (2020): 64–84. http://dx.doi.org/10.4018/ijcac.2020010105.

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Cloud computing has introduced a paradigm which support data outsourcing to third parties for processing using commodity clusters. It allows the owner to outsource sensitive data and share it with the authorized user while reducing the computation and management cost. Since owners store sensitive data over the cloud, the requirements of access control and data security have also been increasing. To alleviate all the problem requirements, the need has arisen for providing a safe, secure, and sound model. The existing solutions for these problems use pure cryptographic techniques, which increase

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Zhu, Binrui, Willy Susilo, Jing Qin, Fuchun Guo, Zhen Zhao, and Jixin Ma. "A Secure and Efficient Data Sharing and Searching Scheme in Wireless Sensor Networks." Sensors 19, no. 11 (2019): 2583. http://dx.doi.org/10.3390/s19112583.

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Wireless sensor networks (WSN) generally utilize cloud computing to store and process sensing data in real time, namely, cloud-assisted WSN. However, the cloud-assisted WSN faces new security challenges, particularly outsourced data confidentiality. Data Encryption is a fundamental approach but it limits target data retrieval in massive encrypted data. Public key encryption with keyword search (PEKS) enables a data receiver to retrieve encrypted data containing some specific keyword in cloud-assisted WSN. However, the traditional PEKS schemes suffer from an inherent problem, namely, the keywor

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Kulkarni, Amogh Pramod, and Manjunath T. N. "Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 146–56. http://dx.doi.org/10.17762/ijritcc.v11i2s.6037.

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Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the quer

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Hussien, Hassan Mansur, Sharifah Md Yasin, Nur Izura Udzir, and Mohd Izuan Hafez Ninggal. "Blockchain-Based Access Control Scheme for Secure Shared Personal Health Records over Decentralised Storage." Sensors 21, no. 7 (2021): 2462. http://dx.doi.org/10.3390/s21072462.

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Blockchain technology provides a tremendous opportunity to transform current personal health record (PHR) systems into a decentralised network infrastructure. However, such technology possesses some drawbacks, such as issues in privacy and storage capacity. Given its transparency and decentralised features, medical data are visible to everyone on the network and are inappropriate for certain medical applications. By contrast, storing vast medical data, such as patient medical history, laboratory tests, X-rays, and MRIs, significantly affect the repository storage of blockchain. This study brid

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Zhang, Yinghui, Haonan Su, Menglei Yang, Dong Zheng, Fang Ren, and Qinglan Zhao. "Secure Deduplication Based on Rabin Fingerprinting over Wireless Sensing Data in Cloud Computing." Security and Communication Networks 2018 (September 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/9081814.

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The rapid advancements in the Internet of Things (IoT) and cloud computing technologies have significantly promoted the collection and sharing of various data. In order to reduce the communication cost and the storage overhead, it is necessary to exploit data deduplication mechanisms. However, existing data deduplication technologies still suffer security and efficiency drawbacks. In this paper, we propose two secure data deduplication schemes based on Rabin fingerprinting over wireless sensing data in cloud computing. The first scheme is based on deterministic tags and the other one adopts ra

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