Splitter: An Efficient Scheme to Determine the Geolocation of Cloud Data Publicly

Abstract

Outsourcing data to the cloud has become a trend, and the geolocation of cloud data attracts public attention in recent years, which is relevant to data availability (e.g., disaster tolerant), data security and policies (e.g. USA Patrio Act). Unfortunately, cloud service providers are not fully trusted to the data owners. This is because the data owners lose the physical control over the cloud data, and cloud service providers have the ability and motivation to change the geolocation of cloud data between different data centers. Therefore, designing a scheme to determine the geolocation of cloud data for data owners is an urgent problem to be solved.In this paper, we propose Splitter, an efficient scheme to determine the geolocation of cloud data publicly. In Splitter, we first design a splitting method, which breaks up the challenge and proof, and only considers the response delay resulting from the general operations (i.e., addition and multiplication) to obtain the accurate response delay. Second, we combine random forest algorithm and improved triangulation method to determine the geolocation accurately. Third, we take a series of theoretical comparison and extensive experiments to evaluate our scheme. The results illustrate the efficiency and practicality of our scheme.