{"title":"MoRePriv: mobile OS support for application personalization and privacy","authors":"Drew Davidson, Matt Fredrikson, B. Livshits","doi":"10.1145/2664243.2664266","DOIUrl":null,"url":null,"abstract":"Privacy and personalization of mobile experiences are inherently in conflict: better personalization demands knowing more about the user, potentially violating user privacy. A promising approach to mitigate this tension is to migrate personalization to the client, an approach dubbed client-side personalization. This paper advocates for operating system support for client-side personalization and describes MoRePriv, an operating system service implemented in the Windows Phone OS. We argue that personalization support should be as ubiquitous as location support, and should be provided by a unified system within the OS, instead of by individual apps. We aim to provide a solution that will stoke innovation around mobile personalization. To enable easy application personalization, MoRePriv approximates users' interests using personae such as technophile or business executive. Using a number of case studies and crowd-sourced user studies, we illustrate how more complex personalization tasks can be achieved using MoRePriv. For privacy protection, MoRePriv distills sensitive user information to a coarse-grained profile, which limits the potential damage from information leaks. We see MoRePriv as a way to increase end-user privacy by enabling client-side computing, thus minimizing the need to share user data with the server. As such, MoRePriv shepherds the ecosystem towards a better privacy stance by nudging developers away from today's privacy-violating practices. Furthermore, MoRePriv can be combined with privacy-enhancing technologies and is complimentary to recent advances in data leak detection.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 30th Annual Computer Security Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2664243.2664266","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 36
Abstract
Privacy and personalization of mobile experiences are inherently in conflict: better personalization demands knowing more about the user, potentially violating user privacy. A promising approach to mitigate this tension is to migrate personalization to the client, an approach dubbed client-side personalization. This paper advocates for operating system support for client-side personalization and describes MoRePriv, an operating system service implemented in the Windows Phone OS. We argue that personalization support should be as ubiquitous as location support, and should be provided by a unified system within the OS, instead of by individual apps. We aim to provide a solution that will stoke innovation around mobile personalization. To enable easy application personalization, MoRePriv approximates users' interests using personae such as technophile or business executive. Using a number of case studies and crowd-sourced user studies, we illustrate how more complex personalization tasks can be achieved using MoRePriv. For privacy protection, MoRePriv distills sensitive user information to a coarse-grained profile, which limits the potential damage from information leaks. We see MoRePriv as a way to increase end-user privacy by enabling client-side computing, thus minimizing the need to share user data with the server. As such, MoRePriv shepherds the ecosystem towards a better privacy stance by nudging developers away from today's privacy-violating practices. Furthermore, MoRePriv can be combined with privacy-enhancing technologies and is complimentary to recent advances in data leak detection.