{"id":158543,"date":"2009-11-20T00:00:00","date_gmt":"2009-11-20T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/anonymity-preserving-data-aggregation-using-anonygator\/"},"modified":"2018-10-16T19:59:16","modified_gmt":"2018-10-17T02:59:16","slug":"anonymity-preserving-data-aggregation-using-anonygator","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/anonymity-preserving-data-aggregation-using-anonygator\/","title":{"rendered":"Anonymity-Preserving Data Aggregation using Anonygator"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Data aggregation is a key aspect of many distributed applications, such as distributed sensing, performance monitoring, and distributed diagnostics. In such settings, user anonymity is a key concern of the participants. In the absence of an assurance of anonymity, users may be reluctant to contribute data such as their location or configuration settings on their computer. In this paper, we present the design, analysis, implementation, and evaluation of Anonygator, an anonymity preserving data aggregation service for large-scale distributed applications. Anonygator uses anonymous routing to provide user anonymity by disassociating messages from the hosts that generated them. It prevents malicious users from uploading disproportionate amounts of spurious data by using a lightweight accounting scheme. Finally, Anonygator maintains overall system scalability by employing a novel distributed tree-based data aggregation procedure that is robust to pollution attacks. All of these components are tuned by a customization tool, with a view to achieve specific anonymity, pollution resistance, and efficiency goals. To demonstrate the usefulness of Anonygator, we have used it to prototype three applications, one of which we have evaluated on PlanetLab. The other two have been evaluated on a local testbed.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data aggregation is a key aspect of many distributed applications, such as distributed sensing, performance monitoring, and distributed diagnostics. In such settings, user anonymity is a key concern of the participants. In the absence of an assurance of anonymity, users may be reluctant to contribute data such as their location or configuration settings on their 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