Distinguishing Group Privacy From Personal Privacy: The Effect of Group Inference Technologies on Privacy Perceptions and Behaviors
Distinguishing Group Privacy From Personal Privacy: The Effect of Group Inference Technologies on Privacy Perceptions and Behaviors
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区分群体隐私和个人隐私：群体推理技术对隐私认知和行为的影响
DOI：
10.1145/3274437
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发表时间：
2018
影响因子：
--
通讯作者：
El Abbadi, Amr
中科院分区：
文献类型：
--
作者：
Suh, Jennifer Jiyoung;Metzger, Miriam J.;Reid, Scott A.;El Abbadi, Amr
机器学习和数据挖掘威胁到个人隐私，并且存在许多工具来帮助用户保护其隐私（例如，Facebook 上可用的隐私设置、个人数据的匿名化和加密等）。但此类技术也对“群体隐私”构成威胁，而学者们对这个概念知之甚少。此外，很少有工具可以解决保护群体隐私的问题。本文讨论了一类新兴的软件应用程序和服务，这些应用程序和服务通过基于个人信息（例如社交媒体帖子或健身应用程序使用）泄露群体级别的信息，对群体隐私构成新的风险。该论文描述了两个实验的结果，这两个实验凭经验建立了群体隐私的概念，并表明它影响用户对信息技术的感知和交互。研究结果呼吁开发人员设计用于群体隐私保护的工具。
Machine learning and data mining threaten personal privacy, and many tools exist to help users protect their privacy (e.g., available privacy settings on Facebook, anonymization and encryption of personal data, etc.). But such technologies also pose threats to "group privacy," which is a concept scholars know relatively little about. Moreover, there are few tools to address the problem of protecting group privacy. This paper discusses an emerging class of software applications and services that pose new risks to group privacy by revealing group-level information based on individual information, such as social media postings or fitness app usage. The paper describes the results of two experiments that empirically establish the concept of group privacy and shows that it affects user perceptions of and interactions with information technology. The findings serve as a call to developers to design tools for group privacy protection.