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-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

scholarly journals Conditional privacy-preserving authentication with access likability for roaming service over internet of things

2018 ◽  
Vol 7 (1.9) ◽  
pp. 34
Author(s):  
P S Uma Priyadarsini ◽  
P Sriramya
Keyword(s):  
Internet Of Things ◽  
Privacy Preservation ◽  
Service Providers ◽  
Privacy Preserving ◽  
Group Signature ◽  
The Internet ◽  
Service Usage ◽  
Internet Service ◽  
Access Information ◽  
The Individual

Today the mobile subscribers can access the internet service whenever they want or wherever they are because of the roaming service. The necessity of accessing pervasively for the developing paradigm of networking such as the Internet of Things (IoT) is accomplished through this facility. In order to provide universal roaming service which is secure and privacy preserving at the multilevel, this paper proposes a privacy-preserving validation which is conditional with access likability called CPAL for roaming service. By utilizing a method of group signature it provides linking function of an anonymous user. This method has the capability to keep the identity of the users concealed and makes the authorized bodies possible to connect all the access information of the same user even without knowing the user’s real identity. In order to connect the access information from the user for enhancing the service, the foreign operators who are authorized or the service providers particularly uses the master linking key possessed by the trust linking server. In order to examine user’s likings, the individual access information is used but user’s identity is not disclosed. Subscribers can further make use of this functionality to probe the service usage without being identified. The proposed method also has the efficiency to simultaneously revoke a group of users. Comprehensive analysis of CPAL demonstrates that it can withstand many security threats and more adjustable in privacy preservation as compared to the other techniques. Assessment of its performance further proves the efficiency of CPAL with regards to communication and computation overhead. Future work would include the extension of CPAL scheme to effectively withstand internal attackers and design the lightweight secure and privacy-preserving scheme that will support IoT devices of large group.

Pattern-Based Cloud Migration

2021 ◽  
pp. 1346-1372
Author(s):  
Zhitao Wan
Keyword(s):  
Service Composition ◽  
Service Selection ◽  
Cloud Service ◽  
Pattern Generation ◽  
Migration Pattern ◽  
Cloud Services ◽  
Legacy Systems ◽  
Cloud Environment ◽  
Business Systems ◽  
Cloud Migration

To migrate on-premises business systems to the cloud environment faces challenges: the complexity, diversity of the legacy systems, cloud, and cloud migration services. Consequently, the cloud migration faces two major problems. The first one is how to select cloud services for the legacy systems, and the second one is how to move the corresponding workload from legacy systems to cloud. This chapter presents a total cloud migration solution including cloud service selection and optimization, cloud migration pattern generation, and cloud migration pattern enforcement. It takes the pattern as the core, and unifies the cloud migration request, the cloud migration service pattern, and the cloud migration service composition. A cloud migration example of blockchain system shows that the proposed approach improves the cloud service selection, cloud migration service composition generation efficiency, migration process parallelization, and enables long transaction support by means of pattern reuse.

scholarly journals Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Xuening Chen ◽  
Hanwen Liu ◽  
Yanwei Xu ◽  
Chao Yan
Keyword(s):  
Service Quality ◽  
Privacy Preservation ◽  
False Negative ◽  
Locality Sensitive Hashing ◽  
Quality Data ◽  
Service Recommendation ◽  
User Privacy ◽  
Target User ◽  
Cross Platform ◽  
Hybrid Collaborative Filtering

Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data. However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results. Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement. Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process. Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake. Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach. The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.

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 Preservation of a Group and Secure Data Storage in Cloud Environment

2015 ◽  
Vol 15 (1) ◽  
pp. 46-54
Author(s):  
K. Govinda ◽  
E. Sathiyamoorthy
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Privacy Preservation ◽  
Personal Information ◽  
Cloud Service ◽  
Cloud Services ◽  
Security And Privacy ◽  
Cloud Environment ◽  
Secure Data

Abstract Cloud computing has become a victorious archetype for data storage, as well as for computation purposes. Greater than ever it concerns user’s privacy, so that data security in a cloud is increasing day by day. Ensuring security and privacy for data organization and query dispensation in the cloud is important for superior and extended uses of cloud based technologies. Cloud users can barely have the full benefits of cloud computing if we can ensure the real user’s privacy and his data security concerns this approach along with storing thin-skinned personal information in databases and software spread around the cloud. There are numerous service suppliers in WWW (World Wide Web), who can supply each service as a cloud. These cloud services will switch over data with a supplementary cloud, so that when the data is exchanged between the clouds, the problem of confidentiality revelation exists. So the privacy revelation problem concerning a person or a corporation is unavoidably open when releasing or data distributing in the cloud service. Confidentiality is a significant issue in any cloud computing environment. In this paper we propose and implement a mechanism to maintain privacy and secure data storage for group members or a community in cloud environment.

scholarly journals Cloud Service Selection as a Fuzzy Multi-criteria Problem

TEM Journal ◽  
2020 ◽  
pp. 484-495 ◽  
Author(s):  
Galina Ilieva ◽  
Tania Yankova ◽  
Vera Hadjieva ◽  
Rositsa Doneva ◽  
George Totkov
Keyword(s):  
Cloud Storage ◽  
Service Selection ◽  
Cloud Service ◽  
Quality Improvements ◽  
Cloud Platform ◽  
Triangular Fuzzy Numbers ◽  
Linguistic Terms ◽  
Customer Support ◽  
Product Features ◽  
Cloud Technologies

Cloud adoption is an attractive technological innovation due to the capital cost reduction and fast quality improvements it provides. In this paper, we present a new fuzzy methodology for cloud service selection. Product features and functionalities, customer support, customer rating, and security options are just a few of the factors influencing cloud platforms evaluation. A practical example for ordering cloud storage systems is calculated by using fuzzy measurement of alternatives and ranking according to the compromise solution (MARCOS) method. After establishing the relevant indicators for cloud technologies’ assessment and their relative weights, crisp values and linguistic terms are transformed into triangular fuzzy numbers and then multi-criteria analysis is employed. The obtained ranking helps managers make an informed and wellgrounded decision for cloud platform selection.

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