Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment

© 2020 Elsevier B.V. With the advance of mobile edge computing (MEC), the number of edge services running on mobile devices grows explosively. In this situation, it is becoming a necessity to recommend the most suitable edge services to a mobile user from massive candidates, based on the historical quality of service (QoS) data. However, historical QoS is a kind of private data for users, which needs to be protected from privacy disclosure. Currently, researchers often use the Locality-Sensitive Hashing (LSH) technique to achieve the goal of privacy-aware recommendations. However, existing LSH-based methods are only applied to the recommendation scenarios with a single QoS dimension (e.g., response time or throughput), without considering the multi-dimensional QoS (e.g., response time and throughput) ensemble, which narrow the application scope of LSH in privacy-preserving recommendations significantly. Considering this drawback, this paper proposes a multi-dimensional quality ensemble-driven recommendation approach named RecLSH-TOPSIS based on LSH and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) techniques. First, the traditional single-dimensional LSH recommendation approach is extended to be a multi-dimensional one, through which we can obtain a set of candidate services that a user may prefer. Second, we use TOPSIS technique to rank the derived multiple candidate services and return the user an optimal one. At last, a case study is presented to illustrate the feasibility of our proposal to make privacy-preserving edge service recommendations with multiple QoS dimensions

From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets

[EN] In recent years, strategies focused on data-driven innovation (DDI) have led to the emergence and development of new products and business models in the digital market. However, these advances have given rise to the development of sophisticated strategies for data management, predicting user behavior, or analyzing their actions. Accordingly, the large-scale analysis of user-generated data (UGD) has led to the emergence of user privacy concerns about how companies manage user data. Although there are some studies on data security, privacy protection, and data-driven strategies, a systematic review on the subject that would focus on both UGD and DDI as main concepts is lacking. Therefore, the present study aims to provide a comprehensive understanding of the main challenges related to user privacy that affect DDI. The methodology used in the present study unfolds in the following three phases; (i) a systematic literature review (SLR); (ii) in-depth interviews framed in the perspectives of UGD and DDI on user privacy concerns, and finally, (iii) topic-modeling using a Latent Dirichlet allocation (LDA) model to extract insights related to the object of study. Based on the results, we identify 14 topics related to the study of DDI and UGD strategies. In addition, 14 future research questions and 7 research propositions are presented that should be consider for the study of UGD, DDI and user privacy in digital markets. The paper concludes with an important discussion regarding the role of user privacy in DDI in digital markets.Saura, JR.; Ribeiro-Soriano, D.; Palacios Marqués, D. (2021). From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets. International Journal of Information Management. 60:1-13. https://doi.org/10.1016/j.ijinfomgt.2021.102331S1136

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

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