Research On Secure Dynamic Skyline Query Processing In The Cloud

With the increase in the amount of data and the development of cloud computing,it is now cost-effective to outsource large datasets and perform query over the cloud.Outsourcing data that require a large amount of computing to the cloud is a very effective computing method.However,in this scenario,there exist serious security and privacy issues that sensitive information contained in the datasets can be leaked.The most effective way to address that is to encrypt the data before outsourcing.Nevertheless,as the name implies,a major responsibility of the cloud lies in ”computing”,and it remains a grand challenge to process queries in a ciphertext efficiently.If the cloud computing platform cannot calculate encrypted data,it cannot take full advantage of cloud computing technology,we urgently need to explore and develop related technologies for computing on encrypted data.In this work,we shall focus on solving one representative query task,namely dynamic skyline query,in a secure manner over the cloud.However,it is difficult to be performed on encrypted data as its dynamic domination criteria require both subtraction and comparison,which cannot be directly supported by a single encryption scheme efficiently.To this end,we present a novel framework called SCALE.It works by transforming traditional dynamic skyline domination into pure comparisons.The whole process can be completed in singleround interaction between user and the cloud.We theoretically prove that the outsourced database,query requests,and returned results are all kept secret under our model.Empirical study over a series of datasets demonstrates that our framework improves the efficiency of query processing by about three orders of magnitude compared to the state-of-the-art.Through experiments,our scheme reached our design goals,and proved the feasibility of the scheme in practical applications.