The Melbourne Shuffle: Improving Oblivious Storage in the Cloud

We present a simple, efficient, and secure data-oblivious randomized shuffle algorithm. This is the first secure data-oblivious shuffle that is not based on sorting. Our method can be used to improve previous oblivious storage solutions for network-based outsourcing of data

A secure data outsourcing scheme based on Asmuth – Bloom secret sharing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

Privacy issues and protection in secure data outsourcing

Utilizing database encryption to safeguard data in several conditions where access control is not sufficient is unavoidable. Database encryption offers an extra layer of security to traditional access control methods. It stops users that are unauthorized, such as hackers breaking into a system, and observing private data. Consequently, data is safe even when the database is stolen or attacked. Nevertheless, the process of data decryption and encryption causes degradation in the database performance. In conditions where the entire information is kept in an encrypted format, it is not possible to choose the database content any longer. The data must be first decrypted, and as such, the unwilling and forced tradeoff occurs between the function and the security. The suitable methods to improve the function are techniques that directly deal with the data that is encrypted without having to decrypt them first. In this study, we determined privacy protection and issues that each organization should consider when it decides to outsource own data

Secure and Reliable Data Outsourcing in Cloud Computing

The many advantages of cloud computing are increasingly attracting individuals and organizations to outsource their data from local to remote cloud servers. In addition to cloud infrastructure and platform providers, such as Amazon, Google, and Microsoft, more and more cloud application providers are emerging which are dedicated to offering more accessible and user friendly data storage services to cloud customers. It is a clear trend that cloud data outsourcing is becoming a pervasive service. Along with the widespread enthusiasm on cloud computing, however, concerns on data security with cloud data storage are arising in terms of reliability and privacy which raise as the primary obstacles to the adoption of the cloud. To address these challenging issues, this dissertation explores the problem of secure and reliable data outsourcing in cloud computing. We focus on deploying the most fundamental data services, e.g., data management and data utilization, while considering reliability and privacy assurance. The first part of this dissertation discusses secure and reliable cloud data management to guarantee the data correctness and availability, given the difficulty that data are no longer locally possessed by data owners. We design a secure cloud storage service which addresses the reliability issue with near-optimal overall performance. By allowing a third party to perform the public integrity verification, data owners are significantly released from the onerous work of periodically checking data integrity. To completely free the data owner from the burden of being online after data outsourcing, we propose an exact repair solution so that no metadata needs to be generated on the fly for the repaired data. The second part presents our privacy-preserving data utilization solutions supporting two categories of semantics - keyword search and graph query. For protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. We define and solve the challenging problem of privacy-preserving multi- keyword ranked search over encrypted data in cloud computing. We establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. We first propose a basic idea for keyword search based on secure inner product computation, and then give two improved schemes to achieve various stringent privacy requirements in two different threat models. We also investigate some further enhancements of our ranked search mechanism, including supporting more search semantics, i.e., TF × IDF, and dynamic data operations. As a general data structure to describe the relation between entities, the graph has been increasingly used to model complicated structures and schemaless data, such as the personal social network, the relational database, XML documents and chemical compounds. In the case that these data contains sensitive information and need to be encrypted before outsourcing to the cloud, it is a very challenging task to effectively utilize such graph-structured data after encryption. We define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing. By utilizing the principle of filtering-and-verification, we pre-build a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure

Secure Cloud Computing for Solving Large-Scale Linear Systems of Equations

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and costeffective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms