{"title":"Consent Management in Data Workflows: A Graph Problem","authors":"Dorota Filipczuk, E. Gerding, G. Konstantinidis","doi":"10.48786/edbt.2023.61","DOIUrl":null,"url":null,"abstract":"Inmoderndataprocessing systemsusersexpectaserviceprovider to automatically respect their consent in all data processing within the service. However, data may be processed for many different purposes by several layers of algorithms that create complex workflows. To date, there is no existing approach to automatically satisfy fine-grained privacy constraints of a user in a way which optimises the service provider’s gains from processing. In this paper, we model a data processing workflow as a graph. User constraints and processing purposes are pairs of vertices which need to be disconnected in this graph. We propose heuristics and algorithms while at the same time we show that, in general, this problem is NP-hard. We discuss the optimality versus efficiency of our algorithms and evaluate them using synthetically generated data. On the practical side, our algorithms can provide a nearly optimal solution in the face of tens of constraints and graphs of thousands of nodes, in a few seconds.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"35 1","pages":"737-748"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in database technology : proceedings. International Conference on Extending Database Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48786/edbt.2023.61","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
Inmoderndataprocessing systemsusersexpectaserviceprovider to automatically respect their consent in all data processing within the service. However, data may be processed for many different purposes by several layers of algorithms that create complex workflows. To date, there is no existing approach to automatically satisfy fine-grained privacy constraints of a user in a way which optimises the service provider’s gains from processing. In this paper, we model a data processing workflow as a graph. User constraints and processing purposes are pairs of vertices which need to be disconnected in this graph. We propose heuristics and algorithms while at the same time we show that, in general, this problem is NP-hard. We discuss the optimality versus efficiency of our algorithms and evaluate them using synthetically generated data. On the practical side, our algorithms can provide a nearly optimal solution in the face of tens of constraints and graphs of thousands of nodes, in a few seconds.