{"title":"Structure Matters - A New Approach for Data Flow Tracking","authors":"Enrico Lovat, Florian Kelbert","doi":"10.1109/SPW.2014.15","DOIUrl":null,"url":null,"abstract":"Usage control (UC) is concerned with how data may or may not be used after initial access has been granted. UC requirements are expressed in terms of data (e.g. a picture, a song) which exist within a system in forms of different technical representations (containers, e.g. files, memory locations, windows). A model combining UC enforcement with data flow tracking across containers has been proposed in the literature, but it exhibits a high false positives detection rate. In this paper we propose a refined approach for data flow tracking that mitigates this over approximation problem by leveraging information about the inherent structure of the data being tracked. We propose a formal model and show some exemplary instantiations.","PeriodicalId":142224,"journal":{"name":"2014 IEEE Security and Privacy Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Security and Privacy Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPW.2014.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Usage control (UC) is concerned with how data may or may not be used after initial access has been granted. UC requirements are expressed in terms of data (e.g. a picture, a song) which exist within a system in forms of different technical representations (containers, e.g. files, memory locations, windows). A model combining UC enforcement with data flow tracking across containers has been proposed in the literature, but it exhibits a high false positives detection rate. In this paper we propose a refined approach for data flow tracking that mitigates this over approximation problem by leveraging information about the inherent structure of the data being tracked. We propose a formal model and show some exemplary instantiations.