Search

Cloud computing is a recent tendency in IT that moves computing and data away from desktop and hand-held devices into large scale processing hubs and data centers respectively. It has been proposed as an effective solution for data outsourcing and on demand computing to control the rising cost of IT setups and management in enterprises. However, with Cloud platforms user's data is moved into remotely located storages such that users lose control over their data. This unique feature of the Cloud is facing many security and privacy challenges which need to be clearly understood and resolved. One of the important concerns that needs to be addressed is to provide the proof of data integrity, i.e., correctness of the user's data stored in the Cloud storage. The data in Clouds is physically not accessible to the users. Therefore, a mechanism is required where users can check if the integrity of their valuable data is maintained or compromised. For this purpose some methods are proposed like mirroring, checksumming and using third party auditors amongst others. However, these methods use extra storage space by maintaining multiple copies of data or the presence of a third party verifier is required. In this paper, we address the problem of proving data integrity in Cloud computing by proposing a scheme through which users are able to check the integrity of their data stored in Clouds. In addition, users can track the violation of data integrity if occurred. For this purpose, we utilize a relatively new concept in the Cloud computing called "Data Provenance". Our scheme is capable to reduce the need of any third party services, additional hardware support and the replication of data items on client side for integrity checking.

Cloud computing revolutionizes data management by offering centralized repositories or services accessible over the Internet. These services, hosted by a single provider or distributed across multiple entities, facilitate seamless access for users and applications. Additionally, cloud technology enables federated search capabilities, allowing organizations to amalgamate data from diverse sources and perform comprehensive searches. However, such integration often leads to challenges in data quality and duplication due to structural disparities among datasets, including variations in metadata. This research presents a novel provenance‐based search model designed to enhance data quality within cloud environments. The model expands the traditional concept of a single canonical URL by incorporating provenance data, thus providing users with diverse search options. Leveraging this model, the study conducts inferential analyses to improve data accuracy and identify duplicate entries effectively. To verify the proposed model, two research paper datasets from Kaggle and DBLP repositories are utilized, and the model effectively identifies duplicates, even with partial queries. Tests demonstrate the system's ability to remove duplicates based on title or author, in both single and distributed dataset scenarios. Traditional search engines struggle with duplicate content, resulting in biased results or inefficient crawling. In contrast, this research uses provenance data to improve search capabilities, overcoming these limitations. [ABSTRACT FROM AUTHOR]