{"id":3089,"date":"2024-04-10T15:16:59","date_gmt":"2024-04-10T15:16:59","guid":{"rendered":"https:\/\/dev.algolysis.eu\/test\/?post_type=research_project&#038;p=3089"},"modified":"2024-08-21T14:18:48","modified_gmt":"2024-08-21T14:18:48","slug":"prinia","status":"publish","type":"research_project","link":"https:\/\/dev.algolysis.eu\/test\/index.php\/research-project\/prinia\/","title":{"rendered":"PRINIA"},"excerpt":{"rendered":"<p>The PRINIA project is an initiative to address the critical need for protecting individual privacy and personal data in the evolving landscape of facial recognition technology with Extended Reality (XR) environments. XR introduces new levels of data collection and interaction, typically involving headsets (e.g., head-mounted displays, HMDs) equipped with cameras and sensors, magnifying the risks to user privacy. PRINIA aims to develop an autonomous module that implements facial recognition in XR environments while ensuring the protection of individual privacy and compliance with relevant European Union legislation and regulations. The project recognizes that the wide adoption of biometric technologies, including facial recognition, has raised significant concerns about data privacy. PRINIA aims to overcome these challenges by adopting a validated research framework and a three-step innovation process:<br \/>\nprivacy rules and compliance management,<br \/>\nfacial recognition supporting privacy enhancement, and<br \/>\nintegration for privacy-preserving XR facial recognition.<br \/>\nThe output of this process is an autonomous module that can be integrated within the SERMAS framework. The module supports various scenarios within XR environments, including individual identification, group categorization, and HMD user identification, ensuring that users can be identified and categorized without compromising privacy or other sensitive data when using immersive technologies.<br \/>\nTo meet its objectives, PRINIA team consists of three SMEs (Human Opsis, Algolysis, and VSC) combining expertise and skills in the intersection of the domains of facial recognition, privacy enhancement, development for XR, and EU law and regulations.<\/p>\n","protected":false},"featured_media":3090,"template":"","project_funding_agency":[57],"project_partner":[],"project_type":[],"research_area":[46,64],"class_list":["post-3089","research_project","type-research_project","status-publish","has-post-thumbnail","hentry","project_funding_agency-57","research_area-ml","research_area-vr-ar"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PRINIA - Algolysis Ltd<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dev.algolysis.eu\/test\/index.php\/research-project\/prinia\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PRINIA - Algolysis Ltd\" \/>\n<meta property=\"og:description\" content=\"The PRINIA project is an initiative to address the critical need for protecting individual privacy and personal data in the evolving landscape of facial recognition technology with Extended Reality (XR) environments. XR introduces new levels of data collection and interaction, typically involving headsets (e.g., head-mounted displays, HMDs) equipped with cameras and sensors, magnifying the risks to user privacy. PRINIA aims to develop an autonomous module that implements facial recognition in XR environments while ensuring the protection of individual privacy and compliance with relevant European Union legislation and regulations. The project recognizes that the wide adoption of biometric technologies, including facial recognition, has raised significant concerns about data privacy. PRINIA aims to overcome these challenges by adopting a validated research framework and a three-step innovation process: privacy rules and compliance management, facial recognition supporting privacy enhancement, and integration for privacy-preserving XR facial recognition. The output of this process is an autonomous module that can be integrated within the SERMAS framework. 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