A secure cloud storage system supporting privacy-preserving fuzzy deduplication

Wen

Security and Communication Networks

et al. 2019

Cloud services are cloud computing resources and applications deployed on the Internet or cloud computing platform, and users can access the required cloud services at any time. However, users face the diversity and complexity of quality of service (QoS) when evaluating and selecting cloud services. Therefore, it is important to study and establish an effective and objective trust model to improve user satisfaction and interaction success rate. In this paper, a model based on combining weights and gray correlation analysis is proposed. Firstly, direct trust, recommendation trust, and reputation together form a comprehensive trust, resulting in a more accurate overall trust. Second, rough set theory and analytic hierarchy process (AHP)-based method are used for the direct trust. Meanwhile, the degree of similarity recommendation trust is calculated by a gray relational analysis method. In order to ensure the accuracy of direct trust, this paper proposes a dynamic trust update mechanism. Finally, the simulation experiment is carried out to verify that the cloud services trust evaluation model (CSTEM) is more robust than the other three methods. It protects against malicious entities; at the same time, it can increase user satisfaction and interaction success rate.

Section: Related Workmentioning

confidence: 99%

“…vmId. E(q)); (6) if(ST > );(7) double =Cloudletlist.getById(cloudletId.vmId).getcloudlet Penalty (); (8) this. IncrementalDirectValue(cloudlet.…”

mentioning

confidence: 99%

A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

Wen

Security and Communication Networks

et al. 2019

“…We highlight detailed differences between our proposed learning model and the existing works. Encryption methods can be employed to address the security and privacy concerns in machine learning 16,17 .…”

Section: Related Workmentioning

confidence: 99%

CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

Çatak¹,

Mustaçoğlu²

2018

IJCIS

The training techniques of the distributed machine learning approach replace the traditional methods with a cloud computing infrastructure and provide flexible computing services to clients. Moreover, machine learning-based classification methods are used in many diverse applications such as medical predictions, speech/face recognition, and financial applications. Most of the application areas require security and confidentiality for both the data and the classifier model. In order to prevent the risk of confidential data disclosure while outsourcing the data analysis, we propose a privacy-preserving protocol approach for the extreme learning machine algorithm and give private classification protocols. The proposed protocols compute the hidden layer output matrix H in an encrypted form by using a distributed multi-party computation (or cloud computing model) approach. This paper shows how to build a privacy-preserving classification model from encrypted data.

Information and Communication Technology

“…So image deduplication is becoming more important. Researchers have pay attention to this field like [20]. We have to handle the images before uploading them to server, a general way is watermarking [21] [22].…”

Section: Related Workmentioning

confidence: 99%

Secure Image Deduplication in Cloud Storage

Yan

2015

Part 8: Multimedia SecurityInternational audienceWith the great development of cloud computing in recent years, the explosive increasing of image data, the mass of information storage, and the application demands for high availability of data, network backup is facing an unprecedented challenge. Image deduplication technology is proposed to reduce the storage space and costs. To protect the confidentiality of the image, the notion of convergent encryption has been proposed. In the deduplication system, the image will be encrypted/ decrypted with a convergent encryption keywhich is derived by computing the hash value of the image content. It means that identical image copies will generate the same ciphertext, which used to check the duplicate image copy. Security analysis makes sure that this system is secure