Biblio

Spreadsheets are widely used in industries for tabular data analysis, visualization and storage. Users often exchange spreadsheets' semi-structured data to collaborative analyze them. Recently, office suites integrated a software module that enables collaborative authoring of office files, including spreadsheets, to facilitate the sharing process. Typically spreadsheets collaborative authoring applications, like Google Sheets or Excel online, need to delocalize the entire file in public cloud storage servers. This choice is not secure for enterprise use because it exposes shared content to the risk of third party access. Moreover, available platforms usually provide coarse grained spreadsheet file sharing, where collaborators have access to all data stored inside a workbook and to all the spreadsheets' formulas used to manipulate those data. This approach limits users' possibilities to disclose only a small portion of tabular data and integrate data coming from different sources (spreadsheets or software platforms). For these reasons enterprise users prefer to control fine grained confidential data exchange and their updates manually through copy, paste, attach-to-email, extract-from-email operations. However unsupervised data sharing and circulation often leads to errors or, at the very least, to inconsistencies, data losses, and proliferation of multiple copies. We propose a model that gives business users a different level of spreadsheet data sharing control, privacy and management. Our approach enables collaborative analytics of tabular data focusing on fine grained spreadsheet data sharing instead of coarse grained file sharing. This solution works with a platform that implements an end to end encrypted protocol for sensitive data sharing that prevents third party access to confidential content. Data are never shared into public clouds but they are transferred encrypted among the administrative domains of collaborators. In this paper we describe the model and the implemented system that enable our solution. We focus on two enterprise use cases we implemented describing how we deployed our platform to speed up and optimize industry processes that involve spreadsheet usage.

Wireless mesh networks (WMNs) are known for possessing good attributes such as low up-front cost, easy network maintenance, and reliable service coverage. This has largely made them to be adopted in various environments such as; school campus networks, community networking, pervasive healthcare, office and home automation, emergency rescue operations and ubiquitous wireless networks. The routing nodes are equipped with self-organized and self-configuring capabilities. However, the routing mechanisms of WMNs depend on the collaboration of all participating nodes for reliable network performance. The authors of this paper have noted that most routing algorithms proposed for WMNs in the last few years are designed with the assumption that all the participating nodes will collaboratively be involved in relaying the data packets originated from a source to a multi-hop destination. Such design approach however exposes WMNs to vulnerability such as malicious packet drop attack. This paper presents an evaluation of the effect of the black hole attack with other influential factors in WMNs. In this study, NS-3 simulator was used with AODV as the routing protocol. The results show that the packet delivery ratio and throughput of WMN under attack decreases sharply as compared to WMN free from attack. On an average, 47.41% of the transmitted data packets were dropped in presence of black hole attack.