Abstract
Database outsourcing is a challenge concerning data secrecy. Even if an adversary, including the service provider, accesses the data, she should not be able to learn any information from the accessed data. In this paper, we address this problem for graph-structured data. First, we define a secrecy notion for graph-structured data based on the concepts of indistinguishability and searchable encryption. To address this problem, we propose an approach based on bucketization. Next to bucketization, it makes use of obfuscated indexes and encryption. We show that finding an optimal bucketization tailored to graph-structured data is NP-hard; therefore, we come up with a heuristic. We prove that the proposed bucketization approach fulfills our secrecy notion. In addition, we present a performance model for scale-free networks which consists of (1) a number-of-buckets model that estimates the number of buckets obtained after applying our bucketization approach and (2) a query-cost model. Finally, we demonstrate with a set of experiments the accuracy of our number-of-buckets model and the efficiency of our approach with respect to query processing.
Highlights
Outsourcing databases to a third-party service provider (SP) has become ubiquitous
Our contributions are: First, we propose a secrecy notion for graphstructured data based on the concepts of indistinguishability [21] and searchable encryption [9]
Our experiments evaluate the tradeoff between secrecy and performance
Summary
While economic and organizational advantages are obvious, database outsourcing remains challenging concerning data secrecy. Distributed and Parallel Databases (2021) 39:35–77 information that needs to be protected against adversaries, including the SP. If an unauthorized user accesses the data, she should not be able to learn anything. Another important trend is that a broad range of real-world datasets exhibits a graph structure. Many real graphs, such as the email network or the Web follow a scale-free power-law distribution [2]. Graphs with this characteristic are the data we focus on in this current paper
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