{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/51047"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/51047","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"A System for Secure and Categorized Video-Sharing","abstract":"Online video sharing is a phenomenon which continues to be increasingly utilized by the entirepopulation. Preserving the privacy of videos shared online is of utmost importance, but there is one use case that hasn’t yet been covered by current mainstream video sharing platforms. The goal of this project was to provide the ability to categorize whether multiple videos are of the same event, so that users can share them only amongst others who were also present at the event and have video evidence. The main method of categorization will be through DNA sequencing, where video files will be converted into literal dna in order to be categorized into 4 categories. This includes those that are of the same event, space, activity, or are completely different videos. The research has shown rather lackluster results that could potentially be further optimized to categorize videos between the 4 categories, let alone whether or not they are of the same event. This paper will introduce and implement multiple methods of doing so, as well as set the stage for future exploration.","abstract_html":"Online video sharing is a phenomenon which continues to be increasingly utilized by the entirepopulation. Preserving the privacy of videos shared online is of utmost importance, but there is one use case that hasn’t yet been covered by current mainstream video sharing platforms. The goal of this project was to provide the ability to categorize whether multiple videos are of the same event, so that users can share them only amongst others who were also present at the event and have video evidence. The main method of categorization will be through DNA sequencing, where video files will be converted into literal dna in order to be categorized into 4 categories. This includes those that are of the same event, space, activity, or are completely different videos. The research has shown rather lackluster results that could potentially be further optimized to categorize videos between the 4 categories, let alone whether or not they are of the same event. 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