Yu-Szu Wei, Xing Wei, Shin-Yi Zheng, Cheng-Hsin Hsu, Chenyang Yang
{"title":"A 6DoF VR Dataset of 3D virtualWorld for Privacy-Preserving Approach and Utility-Privacy Tradeoff","authors":"Yu-Szu Wei, Xing Wei, Shin-Yi Zheng, Cheng-Hsin Hsu, Chenyang Yang","doi":"10.1145/3587819.3592557","DOIUrl":null,"url":null,"abstract":"Virtual Reality (VR) applications offer an immersive user experience at the expense of privacy leakage caused by inevitably streaming various new types of user data. While some privacy-preserving approaches have been proposed for protecting one type of data, how to design and evaluate approaches for multiple types of user data are still open. On the other hand, preserving privacy will degrade the quality of experience of VR applications or say the utility of user data. How to achieve efficient utility-privacy tradeoff with multiple types of data is also open. Both call for a dataset that contains multiple types of user data and personal attributes of users as ground-truth values. In this paper, we collect a 6 degree-of-freedom VR dataset of 3D virtual worlds for the investigation of privacy-preserving approaches and utility-privacy tradeoff.","PeriodicalId":330983,"journal":{"name":"Proceedings of the 14th Conference on ACM Multimedia Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 14th Conference on ACM Multimedia Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3587819.3592557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Virtual Reality (VR) applications offer an immersive user experience at the expense of privacy leakage caused by inevitably streaming various new types of user data. While some privacy-preserving approaches have been proposed for protecting one type of data, how to design and evaluate approaches for multiple types of user data are still open. On the other hand, preserving privacy will degrade the quality of experience of VR applications or say the utility of user data. How to achieve efficient utility-privacy tradeoff with multiple types of data is also open. Both call for a dataset that contains multiple types of user data and personal attributes of users as ground-truth values. In this paper, we collect a 6 degree-of-freedom VR dataset of 3D virtual worlds for the investigation of privacy-preserving approaches and utility-privacy tradeoff.