scholarly journals An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2037 ◽  
Author(s):  
Lianyong Qi ◽  
Shunmei Meng ◽  
Xuyun Zhang ◽  
Ruili Wang ◽  
Xiaolong Xu ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Exception Handling ◽  
Cloud Environment ◽  
Service Recommendation

scholarly journals Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yanwei Xu ◽  
Lianyong Qi ◽  
Wanchun Dou ◽  
Jiguo Yu
Keyword(s):  
Privacy Preservation ◽  
Service Selection ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Service Recommendation ◽  
Service Usage ◽  
Target User ◽  
Usage Data

With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query

2017 ◽  
Vol 96 (2) ◽  
pp. 2305-2322 ◽  
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Bin Wang
Keyword(s):  
Shortest Path ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Shortest Path Query
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