An Anonymity Retaining Framework for Multi-party Skyline Queries Based on Unique Tags

While finding out dominant objects over multiple competing parties’ sensitive datasets via multi-party skyline queries (MSQs), upholding ample data privacy with integrity, data and data owners’ (i.e., parties) anonymity, verifiable deeds of entities, the robustness of framework, etc., is crucial. The existing works consider these criteria trivially or partially and may sacrifice data integrity due to malleable cipher data. This article proposes a framework for MSQs that incorporates these requirements together and eventually makes a ranking of parties’ data-objects. To enrich data privacy, it allocates separate decryption keys among mutual entities. To confirm data integrity, it unites encrypted unique tags (
UT
s) to parties’ encrypted datasets individually. To assure data and parties’ anonymity, it re-encrypts and shuffles encrypted datasets, and uses anonymous credentials, respectively. These qualities and publicly exposed data reinforce the framework and assure the correct deeds of entities. Finally, evaluation results, analyses, comparisons with state-of-the-art works, etc., prove the efficacy of the proposed framework.