Participants will learn to identify and measure the potential consequences of granting smartphone apps different permissions - especially in the light rapid strides in data science that allows us to predict user behavior better and better from more and more innocuous but ubiquitous data.
They will learn how to measure such risks, and how to talk objectively about differing degrees of user preference for privacy.
Agenda
We will share numbers - and techniques to estimate those numbers - around how we can measure privacy risk.
We will also try to make the session interactive to empower participants to grasp and manage their own privacy expectations better. Developers and data scientists will come away with metrics they can begin to track, and non-technical participants will come away with a vocabulary for expressing their privacy preferences and evaluating the risk from apps they install.
Participants
3 participants - dialogue and collective brainstorm around privacy risk from app permissions, perhaps hands-on design of possible new privacy risk metrics
15 participants - hands on exercise in quantifying privacy risk by analyzing the permissions on their favorite apps
25 participants - ask each participant to walk us through their privacy expectations for smartphone apps and their thinking behind it - to give other participants a glimpse of the range of user expectations about privacy and anonymity
Outcome
Participants will be able to -
Understand the privacy implications of app permissions
Learn ways to measure privacy risk
Understand "implied consent" in terms of potential uses or abuses of their personal information collected by apps
Learn about privacy preserving apps
Make more nuanced choices about granting permissions to apps
[ ID ] 4cbb3459-a77c-433f-b261-1b3ee33f45da
[ Submitter's Name ] Suchana Seth
[ Space ] cities [ Secondary Space ] journalism
[ Format ] demo, learning-lab
Description
Participants will learn to identify and measure the potential consequences of granting smartphone apps different permissions - especially in the light rapid strides in data science that allows us to predict user behavior better and better from more and more innocuous but ubiquitous data. They will learn how to measure such risks, and how to talk objectively about differing degrees of user preference for privacy.
Agenda
We will share numbers - and techniques to estimate those numbers - around how we can measure privacy risk. We will also try to make the session interactive to empower participants to grasp and manage their own privacy expectations better. Developers and data scientists will come away with metrics they can begin to track, and non-technical participants will come away with a vocabulary for expressing their privacy preferences and evaluating the risk from apps they install.
Participants
3 participants - dialogue and collective brainstorm around privacy risk from app permissions, perhaps hands-on design of possible new privacy risk metrics 15 participants - hands on exercise in quantifying privacy risk by analyzing the permissions on their favorite apps 25 participants - ask each participant to walk us through their privacy expectations for smartphone apps and their thinking behind it - to give other participants a glimpse of the range of user expectations about privacy and anonymity
Outcome
Participants will be able to -