Poli-see

Guo, Wentao; Rodolitz, Jay; Birrell, Eleanor

doi:10.1145/3411497.3420221

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…Various design dimensions, e.g., the timing of notices, should be accounted for [67]. Poli-see explored how to best visualize privacy notices [34]. Concise and salient representations are promising [23].…”

Section: Privacy Noticesmentioning

confidence: 99%

Website Data Transparency in the Browser

Zimmeck,

Goldelman,

Kaplan

et al. 2024

PoPETs

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Data collection by websites and their integrated third parties is often not transparent. We design privacy interfaces for the browser to help people understand who is collecting which data from them. In a proof of concept browser extension, Privacy Pioneer, we implement a privacy popup, a privacy history interface, and a watchlist to notify people when their data is collected. For detecting location data collection, we develop a machine learning model based on TinyBERT, which reaches an average F1 score of 0.94. We supplement our model with deterministic methods to detect trackers, collection of personal data, and other monetization techniques. In a usability study with 100 participants 82% found Privacy Pioneer easy to understand and 90% found it useful indicating the value of privacy interfaces directly integrated in the browser.

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Section: Privacy Noticesmentioning

confidence: 99%

Website Data Transparency in the Browser

Zimmeck,

Goldelman,

Kaplan

et al. 2024

PoPETs

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“…Other tools, such as Data Track [10] show the disclosure of users' data to service providers and address data access stored on the service's side. Poli-see [70] extended Data Track and addresses data type, data usage, data transformation, data collector and third parties, and any available configuration options. Lomotey et al [101] proposed Data Trusts as a Service (DTaaS), a cloud-based platform that facilitates data sharing among multiple partners.…”

Section: User-centered Privacy Management In the Web And Mobile Eramentioning

confidence: 99%

“…Privacy Bird [41] PrivacyCheck [161] Privee [169] CPPs [119] Nutrition Label for privacy [83] PrimeLife [54] Send data [11] VIPP [130] PPO [108] SweetDroid [31] Prihook [152] PPVM [62] Data Track [10] Poli-see [70] Privacy Wheel [153] TransparencyVis [140] Privacy Facts [84] Habib et al [72] Privacy Booklets [125] PEOPLEFINDER [136] Scipioni and Langheinrich [ PILOT [122] IoT Refine [154] Transparency app [50] Data type (What?) Defines collected data type.…”

Section: Web and Mobile Iotmentioning

confidence: 99%

“…Various research used circular or wheel framing to visualise web privacy policies in a more concise graphical representation. Data Track [10] and Poli-see [70], used coloured nodes to represent data flow in privacy policy visualisations. By hovering over the nodes, users can learn more about the data and perform configuration if applicable.…”

Section: 12mentioning

confidence: 99%

“…for users to configure their privacy choices. Poli-see [70] added a hovering interaction feature that conveys configuration and privacy information to the user. Salgado et al [44] reformed Nutrition label for privacy symbols into coloured switches to simplify configuring privacy preferences for mobile users.…”

Section: Interactionmentioning

confidence: 99%

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Interactive Privacy Management: Toward Enhancing Privacy Awareness and Control in the Internet of Things

Muhander

Wiese

Rana

et al. 2023

ACM Trans. Internet Things

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The balance between protecting user privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT). While in traditional desktop and mobile contexts, the primary user interface is a screen, in IoT devices, screens are rare or very small, invalidating many existing approaches to protecting user privacy. Privacy visualisations are a common approach for assisting users in understanding the privacy implications of web and mobile services. To gain a thorough understanding of IoT privacy, we examine existing web, mobile, and IoT visualisation approaches. Following that, we define five major privacy factors in the IoT context: (i) type, (ii) usage, (iii) storage, (iv) retention period, and (v) access. We then describe notification methods used in various contexts as reported in the literature. We aim to highlight key approaches that developers and researchers can use for creating effective IoT privacy notices that improve user privacy management (awareness and control). Using a toolkit, a use case scenario, and two examples from the literature, we demonstrate how privacy visualisation approaches can be supported in practice.

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