Systemic Risk for Privacy in Online Interaction
This project is related to our research line: Online Social Networks
Duration 12 months (January 2017 - December 2017)
Funding source ETH Zurich Foundation, through ETH Risk Center
The goal of this project is to understand the systemic dimension of privacy risks emerging from online interaction. Today, digital traces generated by millions of users allow to infer private attributes of individuals that may not even use online media. This poses a considerable risk to privacy which needs to be conceptualized, quantified and measured.
Hence, as a first step, we will explore ways to construct shadow profiles, i.e. aggregated information about individuals that was not provided by them, but inferred from the information disclosed by other users and their interactions. This allows us to reveal the conditions under which privacy risks can occur, to eventually mitigate them.
As a second step, we will developmodeling framework to test how social mechanisms are able to exacerbate this privacy risk. For example, the decisions of others to make their information public can be also driven by herding effects. Hence, in social networks there is an impact of the collective dynamics on the degree of privacy risk.
Figure: Schema of a full shadow profile construction problem (adapted from Sarigöl, Emre and Garcia, David and Schweitzer, Frank, 2014, "Online Privacy as a Collective Phenomenon")
Eventually, we want to explore how the increase of privacy risk leads to an increasing risk of the social network to collapse, because users may decide to leave or to not join the network. This implies to understand the systemic feedback between individual decisions and the resilience of the network, and has potential impact to help users to manage privacy, to inform policy makers in regulating online data use, and to aid online community designers to achieve a balance between information access and privacy.
With our research we aim at contributing to the following research questions: