Title
Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records.
Abstract
In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users’ private information contained in historical service invocation records.
Year
DOI
Venue
2019
10.1186/s13638-019-1432-2
EURASIP Journal on Wireless Communications and Networking
Keywords
Field
DocType
Service recommendation, Historical service invocation records, Simhash, Multi-probe, Privacy-preservation
Recommender system,World Wide Web,Service quality,Computer science,Invocation,Computer network,Big data,Private information retrieval,Cloud computing,Scalability
Journal
Volume
Issue
ISSN
2019
1
1687-1499
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Qiang Wei113330.22
Wenxue Wang296.23
Gongxuan Zhang39419.89
Tingting Shao400.34