On the security of NoSQL cloud database services

Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a public cloud infrastructure where, the Cloud Service Provider (CSP) is not completely trusted by the data owner. We present a high level description of several architectures combining modern cryptographic primitives for achieving this goal. A novel searchable security scheme is proposed to leverage secure query processing in presence of a malicious cloud insider without disclosing sensitive information. A holistic database security scheme comprised of data confidentiality and information leakage prevention is proposed in this dissertation. The main contributions of our work are: (i) A searchable security scheme for non-relational databases of the cloud DBaaS; (ii) Leakage minimization in the untrusted cloud. The analysis of experiments that employ a set of established cryptographic techniques to protect databases and minimize information leakage, proves that the performance of the proposed solution is bounded by communication cost rather than by the cryptographic computational effort

Privacy, Access Control, and Integrity for Large Graph Databases

Graph data are extensively utilized in social networks, collaboration networks, geo-social networks, and communication networks. Their growing usage in cyberspaces poses daunting security and privacy challenges. Data publication requires privacy-protection mechanisms to guard against information breaches. In addition, access control mechanisms can be used to allow controlled sharing of data. Provision of privacy-protection, access control, and data integrity for graph data require a holistic approach for data management and secure query processing. This thesis presents such an approach. In particular, the thesis addresses two notable challenges for graph databases, which are: i) how to ensure users\u27 privacy in published graph data under an access control policy enforcement, and ii) how to verify the integrity and query results of graph datasets. To address the first challenge, a privacy-protection framework under role-based access control (RBAC) policy constraints is proposed. The design of such a framework poses a trade-off problem, which is proved to be NP-complete. Novel heuristic solutions are provided to solve the constraint problem. To the best of our knowledge, this is the first scheme that studies the trade-off between RBAC policy constraints and privacy-protection for graph data. To address the second challenge, a cryptographic security model based on Hash Message Authentic Codes (HMACs) is proposed. The model ensures integrity and completeness verification of data and query results under both two-party and third-party data distribution environments. Unique solutions based on HMACs for integrity verification of graph data are developed and detailed security analysis is provided for the proposed schemes. Extensive experimental evaluations are conducted to illustrate the performance of proposed algorithms

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed