Abstract
Data outsourcing allows data owners to keep their data at \emph{untrusted} clouds that do not ensure the privacy of data and/or computations. One useful framework for fault-tolerant data processing in a distributed fashion is MapReduce, which was developed for \emph{trusted} private clouds. This paper presents algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries such as count, selection including range selection, projection, and join while using MapReduce as an underlying programming model. Our proposed algorithms prevent an adversary from knowing the database or the query while also preventing output-size and access-pattern attacks. Interestingly, our algorithms do not involve the database owner, which only creates and distributes secret-shares once, in answering any query, and hence, the database owner also cannot learn the query. Logically and experimentally, we evaluate the efficiency of the algorithms on the following parameters: (\textit{i}) the number of communication rounds (between a user and a server), (\textit{ii}) the total amount of bit flow (between a user and a server), and (\textit{iii}) the computational load at the user and the server.\B
Highlights
The past few years have witnessed a huge amount of sensitive data generation due to several applications, e.g., location tracking sensors, web crawling, social networks, and body-area networks.Such real-time data assists users in several ways such as suggesting new restaurants, music, videos, alarms for health checkups based on the user’s history; it carries a potential threat to the user’s privacy
The database outsourcing to public servers is a prominent solution to deal with a resource-constrained database owner and avoid overheads for maintaining and executing queries at the database owner
This paper presented information-theoretically secure data and computation outsourcing techniques, especially, algorithms for count, selection, projection, join, and range queries, while using MapReduce as an underlying programming model
Summary
The past few years have witnessed a huge amount of sensitive data generation due to several applications, e.g., location tracking sensors, web crawling, social networks, and body-area networks. Some works based on encryption [4], [5], [6], [7], [8] and trusted hardware [9], [10] have been proposed to execute MapReduce computations in a secure and privacy-preserving manner at the cloud. We provide Shamir’s secret-sharing (SSS) [17] based informationtheoretically secure data and computation outsourcing technique that prevents an adversary from knowing the database or the query. We perform some more work on all the shares, the DB owner (not the servers) work at the server due to our secret-sharing-based data encoding incurs the overhead of secure computing; and (ii) a third-party technique. Similar ideas can We assume the following three entities in our model; see Figure 1: be found in [20]
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