English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Wearable devices have gained huge popularity in today's world. These devicescollect large-scale health data from their users, such as heart rate and stepcount data, that is privacy sensitive, however it has not yet received thenecessary attention in the academia. In this paper, we perform the firstsystematic study on quantifying privacy risks stemming from step count data. Inparticular, we propose two attacks including attribute inference for gender,age and education and temporal linkability. We demonstrate the severity of theprivacy attacks by performing extensive evaluation on a real life dataset andderive key insights. We believe our results can serve as a step stone forderiving a privacy-preserving ecosystem for wearable devices in the future.

You Are How You Walk: Quantifying Privacy Risks in Step Count Data