A Framework for Multi-party Skyline Query Maintaining Privacy and Data Integrity

Abstract

Skyline query is well-known to find out the dominant objects from a large number of datasets. While multiple organizations want to analyze their combined dataset, skyline queries can assist in this regard. Maintaining privacy along with the data integrity of participating organizations’ datasets is important because their commercial success depends on the result of these queries. This paper proposes a new framework for the multi-party skyline query that encompasses both privacy and data integrity. To ensure the privacy of participants’ datasets, it adopts commutative encryptions by employing multiple independent entities. To support the data integrity, it combines encrypted unique tags (UTs) with the encrypted datasets of all participants. In addition, to retain the anonymity of participants’ encrypted data from anyone including authorities, it exploits the re-encryption. Although the proposed framework also practices homomorphic encryption, which usually sacrifices the data integrity, here due to the usage of UTs, it is maintained. This paper is a preliminary report of the proposed framework.