CEPTM: A Cross-Edge Model for Diverse Personalization Service and Topic Migration in MEC

Abstract EN

For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues.

MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem.

As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents.

This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization.

This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service.

In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization.

It outperforms a traditional personalization service that runs on a single-edge server.

This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.