Toward efficient and secure public auditing for dynamic big data storage on cloud

University of Technology Sydney. Faculty of Engineering and Information Technology.Cloud and Big Data are two of the most attractive ICT research topics that have emerged in recent years. Requirements of big data processing are now everywhere, while the pay-as-you-go model of cloud systems is especially cost efficient in terms of processing big data applications. However, there are still concerns that hinder the proliferation of cloud, and data security/privacy is a top concern for data owners wishing to migrate their applications into the cloud environment. Compared to users of conventional systems, cloud users need to surrender the local control of their data to cloud servers. Another challenge for big data is the data dynamism which exists in most big data applications. Due to the frequent updates, efficiency becomes a major issue in data management. As security always brings compromises in efficiency, it is difficult but nonetheless important to investigate how to efficiently address security challenges over dynamic cloud data. Data integrity is an essential aspect of data security. Except for server-side integrity protection mechanisms, verification from a third-party auditor is of equal importance because this enables users to verify the integrity of their data through the auditors at any user-chosen timeslot. This type of verification is also named 'public auditing' of data. Existing public auditing schemes allow the integrity of a dataset stored in cloud to be externally verified without retrieval of the whole original dataset. However, in practice, there are many challenges that hinder the application of such schemes. To name a few of these, first, the server still has to aggregate a proof with the cloud controller from data blocks that are distributedly stored and processed on cloud instances and this means that encryption and transfer of these data within the cloud will become time-consuming. Second, security flaws exist in the current designs. The verification processes are insecure against various attacks and this leads to concerns about deploying these schemes in practice. Third, when the dataset is large, auditing of dynamic data becomes costly in terms of communication and storage. This is especially the case for a large number of small data updates and data updates on multi-replica cloud data storage. In this thesis, the research problem of dynamic public data auditing in cloud is systematically investigated. After analysing the research problems, we systematically address the problems regarding secure and efficient public auditing of dynamic big data in cloud by developing, testing and publishing a series of security schemes and algorithms for secure and efficient public auditing of dynamic big data storage on cloud. Specifically, our work focuses on the following aspects: cloud internal authenticated key exchange, authorisation on third-party auditor, fine-grained update support, index verification, and efficient multi-replica public auditing of dynamic data. To the best of our knowledge, this thesis presents the first series of work to systematically analysis and to address this research problem. Experimental results and analyses show that the solutions that are presented in this thesis are suitable for auditing dynamic big data storage on cloud. Furthermore, our solutions represent significant improvements in cloud efficiency and security

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Spatial Data Quality in the IoT Era:Management and Exploitation

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications