358 research outputs found
"If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps
Cloud storage services, like Dropbox and Google Drive, have growing
ecosystems of 3rd party apps that are designed to work with users' cloud files.
Such apps often request full access to users' files, including files shared
with collaborators. Hence, whenever a user grants access to a new vendor, she
is inflicting a privacy loss on herself and on her collaborators too. Based on
analyzing a real dataset of 183 Google Drive users and 131 third party apps, we
discover that collaborators inflict a privacy loss which is at least 39% higher
than what users themselves cause. We take a step toward minimizing this loss by
introducing the concept of History-based decisions. Simply put, users are
informed at decision time about the vendors which have been previously granted
access to their data. Thus, they can reduce their privacy loss by not
installing apps from new vendors whenever possible. Next, we realize this
concept by introducing a new privacy indicator, which can be integrated within
the cloud apps' authorization interface. Via a web experiment with 141
participants recruited from CrowdFlower, we show that our privacy indicator can
significantly increase the user's likelihood of choosing the app that minimizes
her privacy loss. Finally, we explore the network effect of History-based
decisions via a simulation on top of large collaboration networks. We
demonstrate that adopting such a decision-making process is capable of reducing
the growth of users' privacy loss by 70% in a Google Drive-based network and by
40% in an author collaboration network. This is despite the fact that we
neither assume that users cooperate nor that they exhibit altruistic behavior.
To our knowledge, our work is the first to provide quantifiable evidence of the
privacy risk that collaborators pose in cloud apps. We are also the first to
mitigate this problem via a usable privacy approach.Comment: Authors' extended version of the paper published at CODASPY 201
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