TL
T.J. Langhout
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One distinguishable feature of file-inject attacks on searchable encryption schemes is the 100% query recovery rate, i.e., confirming the corresponding keyword for each query. The main efficiency consideration of file-injection attacks is the number of injected files. In the work of Zhang et al. (USENIX 2016), log_2|K| injected files are required, each of which contains |K|/2 keywords for the keyword set K. Based on the construction of the uniform (s,n)-set, Wang et al. need fewer injected files when considering the threshold countermeasure. In this work, we propose a new attack that further reduces the number of injected files where Wang et al. need up to 38% more injections to achieve the same results. The attack is based on an increment (s,n)-set, which is also defined in this paper.
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One distinguishable feature of file-inject attacks on searchable encryption schemes is the 100% query recovery rate, i.e., confirming the corresponding keyword for each query. The main efficiency consideration of file-injection attacks is the number of injected files. In the work of Zhang et al. (USENIX 2016), log_2|K| injected files are required, each of which contains |K|/2 keywords for the keyword set K. Based on the construction of the uniform (s,n)-set, Wang et al. need fewer injected files when considering the threshold countermeasure. In this work, we propose a new attack that further reduces the number of injected files where Wang et al. need up to 38% more injections to achieve the same results. The attack is based on an increment (s,n)-set, which is also defined in this paper.
B2B Customer Insight Tool
Automated Data Analytics to improve the Deal Analytics workflow
Bachelor thesis
(2020)
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T.J. Langhout, S.A.J. van Leeuwen, C.I. Ort, W.S. Volkers, D.H.J. Epema, M. Kerkhof, L. Gunneweg, T. Boevink
The Deal Analytics group of PricewaterhouseCoopers Amsterdam has requested a tool for automatising the business-to-business customer analysis. This analysis was performed manually, which left room for performance improvement. This report discusses how the a product was developed which automates the analyses After two weeks of initial research, a complete system was designed and implemented in the subsequent nine weeks. The tool consists of two distinct parts: a front-end and a back-end. The front-end allows the user to customise the analysis to its own preferences, and communicates with the back-end to efficiently perform the analysis. With the help of user evaluations, the front-end has been designed such that it is usable by any PwC employee within the Deals branch.The back-end uses data analysis techniques and machine learning to analyse customer behaviour. Strong points and growth opportunities of a company are found using techniques such as customer segmentation, regression analysis, and cross-sell analysis. The product has been tested using a variety of techniques to ensure that the software does not crash on unexpected input. The final product is evaluated based on the requirements, design goals and success criteria set at the start of the project and can be considered successful.
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The Deal Analytics group of PricewaterhouseCoopers Amsterdam has requested a tool for automatising the business-to-business customer analysis. This analysis was performed manually, which left room for performance improvement. This report discusses how the a product was developed which automates the analyses After two weeks of initial research, a complete system was designed and implemented in the subsequent nine weeks. The tool consists of two distinct parts: a front-end and a back-end. The front-end allows the user to customise the analysis to its own preferences, and communicates with the back-end to efficiently perform the analysis. With the help of user evaluations, the front-end has been designed such that it is usable by any PwC employee within the Deals branch.The back-end uses data analysis techniques and machine learning to analyse customer behaviour. Strong points and growth opportunities of a company are found using techniques such as customer segmentation, regression analysis, and cross-sell analysis. The product has been tested using a variety of techniques to ensure that the software does not crash on unexpected input. The final product is evaluated based on the requirements, design goals and success criteria set at the start of the project and can be considered successful.