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.
Highlights
With the advent of the big data age, the volume and variety of available data both increase quickly, which make it hard for a user to select valuable information that matches his/her preferences [1,2,3,4]
Through analyzing the service lists ever-executed or ever-invoked by historical users, a recommender system, such as the collaborative filtering (CF) recommender system, can infer the possible user preferences and find the users who are similar with a target user; afterward, appropriate new services are recommended to the target user according to the service list ever-executed by his/her similar friends
The reason is (1) WSRec and ICF are mainly collaborative filtering-based neighbor search methods and cannot avoid too many or too few returned neighboring users or neighboring services, while too many or too few returned neighbors for recommendation decision-makings may fluctuate or decrease the recommendation accuracy; (2) DistSRLSH is more suitable for protecting the Quality of service (QoS) values that are real number instead of the historical service invocation records (Boolean values) that we focus on in this paper
Summary
With the advent of the big data age, the volume and variety of available data both increase quickly, which make it hard for a user to select valuable information that matches his/her preferences [1,2,3,4]. While traditional CF methods often fail to deliver a quick and accurate recommended list as user similarity or item similarity needs to be calculated repeatedly when the historical service invocation records are updated frequently Considering these drawbacks, the Simhash technique that is popular in privacy-aware information retrieval is introduced into the recommendation domain. In [25], the sensitive QoS data are firstly split into multiple pieces (i.e., QoS pieces); and the QoS pieces are sent to different users for storage; the less-sensitive QoS pieces are utilized as the service recommendation bases This method can achieve a partial privacy-preservation goal in service recommendation; it still fails to protect other key user privacy information, e.g., the set of web services that were executed by different users in the past.
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