CONTINUITY OF (Α,β)-Derivatio OF OPERATOR ALGEBRAS

Recommender systems have become a popular and effective way to quickly discover new service items that are probably preferred by prospective users. Through analyzing the historical service usage data produced in the past, a recommender system can infer the potential user preferences and make accurate recommendations accordingly. However, in the edge environment, the service usage data stored in each edge server are often very sparse, which may result in expected cold-start problems. Besides, in the edge environment, the data required to make an optimal service recommendation decision are often stored in different edge clients or servers, which require additional privacy-preservation strategies to secure the sensitive data involved. Considering the above two drawbacks, traditional locality-sensitive hashing (LSH) is improved to be multi-probing LSH and then introduced to aid the recommendation process so as to guarantee the security and robustness of recommender systems. Experiments conducted on well-known dataset prove the effectiveness and efficiency of the work.

Section: Discussionmentioning

confidence: 99%

Improved LSH for privacy-aware and robust recommender system with sparse data in edge environment

Chen

Yang

2019

“…(2) In the experiment test, only one type of decision-making data (i.e., Boolean service invocation records) is considered to judge whether two users are similar neighbors. In the future, we will extend our method to accommodate the diversity of data types in the big data environment, e.g., discrete values [46][47][48][49][50][51][52], continuous values [53][54][55][56][57], fuzzy values [58], and so on.…”

Section: Further Discussionmentioning

confidence: 99%

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

Wei

Wang²,

Zhang

et al. 2019

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.

“…2. Second, only the dimension with Boolean values is tested without considering the diversity of data types, such as the discrete data [42][43][44][45][46][47][48][49][50][51][52], fuzzy data [53][54][55], and continuous data [56][57][58][59][60][61][62][63], as well as their integration issues. 3.…”

Section: Discussionmentioning

confidence: 99%

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

Yan

2019

The ever-increasing popularity of mobile devices (e.g., mobile phones and smart watches) has created a variety of crowdsourcing applications by employing the massive and distributed mobile computing resources. Typically, a task requester sends his/her task request and constraint conditions to a crowdsourcing platform, and then the crowdsourcing platform is responsible for finding a set of appropriate workers (e.g., mobile users) from massive candidates to satisfy the task request. However, for a mobile crowdsourcing task being executed by a set of workers, a pre-selected worker may become unavailable due to various exceptions. In this situation, it is significant for the crowdsourcing platform to quickly find another similar worker to replace the unavailable worker so as to smooth the crowdsourcing process. However, the above exception handling process is often challenging as candidate workers are often not willing to release their sensitive information to the platform due to privacy concerns. In view of this challenge, in this paper, a novel privacy-preserving exception handling approach, named ExH Simhash , is put forward based on Simhash technique. Finally, through a set of simulated experiments, we validate the feasibility of ExH Simhash in terms of substitution equivalence and computational time.