Secure k-objects selection for a keyword query based on MapReduce skyline algorithm
Asif Zaman, Md. Anisuzzaman Siddique, N.A. Annisa, Yasuhiko Morimoto · International Journal of Computational Science and Engineering · 2018
Keyword query interface has become a de-facto standard and such systems have been used by the community for decades. The process of selecting necessary objects for a keyword query is considered as one of the most precious query problems. In top-k query, a user specifies scoring functions and k, the number of objects to be retrieved. Based on the user's scoring function, k-objects are then selected. However, the top-k objects are valuable only for users whose scoring functions are similar. In some cases, parties may not want to disclose any information during the processing. In this paper, we propose k-object selection procedure that selects various k-objects that are preferable for all users whose scoring functions are not identical. The proposed method prevents disclosures of sensitive information. The idea of skyline and top-k query along with perturbed cipher has been used to select the k-objects securely by using MapReduce framework.