Privacy preserving software engineering for data driven development

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dc.contributor.authorTongay, Karan Naresh
dc.contributor.supervisorErnst, Neil A.
dc.date.accessioned2020-12-15T04:57:41Z
dc.date.available2020-12-15T04:57:41Z
dc.date.copyright2020en_US
dc.date.issued2020-12-14
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe exponential rise in the generation of data has introduced many new areas of research including data science, data engineering, machine learning, artificial in- telligence to name a few. It has become important for any industry or organization to precisely understand and analyze the data in order to extract value out of the data. The value of the data can only be realized when it is put into practice in the real world and the most common approach to do this in the technology industry is through software engineering. This brings into picture the area of privacy oriented software engineering and thus there is a rise of data protection regulation acts such as GDPR (General Data Protection Regulation), PDPA (Personal Data Protection Act), etc. Many organizations, governments and companies who have accumulated huge amounts of data over time may conveniently use the data for increasing business value but at the same time the privacy aspects associated with the sensitivity of data especially in terms of personal information of the people can easily be circumvented while designing a software engineering model for these types of applications. Even before the software engineering phase for any data processing application, often times there can be one or many data sharing agreements or privacy policies in place. Every organization may have their own way of maintaining data privacy practices for data driven development. There is a need to generalize or categorize their approaches into tactics which could be referred by other practitioners who are trying to integrate data privacy practices into their development. This qualitative study provides an understanding of various approaches and tactics that are being practised within the industry for privacy preserving data science in software engineering, and discusses a tool for data usage monitoring to identify unethical data access. Finally, we studied strategies for secure data publishing and conducted experiments using sample data to demonstrate how these techniques can be helpful for securing private data before publishing.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/12446
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectData Privacyen_US
dc.subjectPrivacyen_US
dc.subjectData Engineeringen_US
dc.subjectSoftware Engineeringen_US
dc.subjectData Driven Developersen_US
dc.subjectData Scienceen_US
dc.subjectPrivacy Preservingen_US
dc.subjectData Driven Developmenten_US
dc.subjectMachine Learningen_US
dc.subjectOne class SVMen_US
dc.subjectData Usage Monitoringen_US
dc.subjectHealth dataen_US
dc.subjectk-anonymityen_US
dc.subjectl-diversityen_US
dc.subjectdifferential privacyen_US
dc.subjectInformation managementen_US
dc.subjectSecure data sharingen_US
dc.subjectSurveyen_US
dc.subjectAudits and access controlen_US
dc.subjectData Privacy Tacticsen_US
dc.titlePrivacy preserving software engineering for data driven developmenten_US
dc.typeThesisen_US

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