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:

  • How is the decision of a user to not share private information affected by the online behavior and the connections of others?
  • What is the tipping point beyond which individuals have a negligible privacy even if they did not reveal personal information, because their shadow profile
    can be completed by social inference?
  • If the loss of privacy can be perceived by users, how will that affect the resilience of an online social network?
     

 

Selected publications

Understanding Popularity, Reputation, and Social Influence in the Twitter Society

[2016]
Garcia, David; Mavrodiev, Pavlin; Casati, Daniele; Schweitzer, Frank

Policy & Internet

more»

Online Privacy as a Collective Phenomenon

[2014]
Sarigol, Emre; Garcia, David; Schweitzer, Frank

In Proceedings of the 2nd Conference on Online Social Networks 2014

more»