Quantification of perceived location privacy, and its relationship to privacy behaviour

感知位置隐私的量化及其与隐私行为的关系

• 批准号: 409241470
• 负责人: Professor Dr.-Ing. Sebastian Möller
• 金额: --
• 依托单位: Quality and Usability Lab
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/409241470?language=en
• 关键词: Quantification; perceived; location; privacy; its

Privacy is an increasingly important topic for information and communication technologies. Massive amounts of data are collected from the users' online activities and mobile device usage, and aggregated to form information which can affect a user's privacy. Although mechanisms exist to control access to such information, it is mostly impossible for a common user to understand and control the information which can be extracted from collected data. Location information is of particular concern in this context, as from seemingly anonymous location data vast amounts of information can be inferred.Privacy preferences that users have when using location-based systems can typically be measured on a dichotomous level: either the users share their location in a certain situation or they do not. However, the users' actual privacy preferences are likely more fine-grained than this. It is the goal of this research project to (1) understand and quantify such preferences, (2) to understand how they relate to overt sharing behavior, and (3) to make this knowledge accessible through formal modelling. The first goal is reached by assessing fine-grained privacy preferences by a method of quantified value assignment. The second goal is reached by empirically studying how this quantified location privacy valuation is reflected in actual privacy behavior, taking the users' knowledge and awareness of location sharing into account. The third goal is reached by adapting existing behavioural models to formalize privacy-related behaviour in a mobile context.

隐私是信息和通信技术的越来越重要的主题。从用户的在线活动和移动设备的使用中收集了大量数据，并汇总以形成可能影响用户隐私的信息。尽管存在控制访问此类信息的机制，但普通用户大多不可能理解和控制可以从收集的数据中提取的信息。在这种情况下，位置信息特别关注，因为可以推断出大量信息，从看似匿名的位置数据中可以推断出大量信息。用户使用基于位置的系统时的私人偏好通常可以在二分法级别进行测量：用户要么在某些情况下共享其位置，要么没有。但是，用户的实际隐私偏好可能比这更细粒。该研究项目的目标是（1）了解和量化此类偏好，（2）了解它们如何与公开共享行为相关联，以及（3）通过正式建模使该知识可以访问。通过通过量化价值分配方法评估细粒度的隐私偏好来实现第一个目标。通过经验研究如何在实际隐私行为中反映出这种量化的位置隐私估值，从而将用户的知识和对位置共享的认识的认识来实现，实现了第二个目标。通过调整现有的行为模型以在移动环境中形式化与隐私相关的行为正式化来实现第三个目标。