Increasing Transparency and Privacy for Online Social Network Users – USEMP Value Model, Scoring Framework and Legal

Popescu, Andrei; Hildebrandt, Mireille; Breuer, Jonas; Claeys, Laurence; Papadopoulos, Symeon; Petkos, Georgios; Michalareas, Theodoros; Lund, David J.; Heyman, Rob; Graaf, Shenja van der; Gadeski, E.; Borgne, H. Le; Devries, Kelly; Kastrinogiannis, Timotheos; Kousaridas, Apostolos; Padyab, Ali Mohammad

doi:10.1007/978-3-319-31456-3_3

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…In other research the DataBait tool 4 was developed with a similar objective. Based on user-data donations by users of social networks, we hoped to provide them with the kind of profiles that could be drawn about them, with a range of sensitive attributes [43]. Though the tool was not operational by the time the project finished, it might have generated similar attention from Facebook if it had been successful.…”

Section: Intermezzo On Counter Profilingmentioning

confidence: 99%

The Issue of Proxies, And why EU law matters for recommender systems

Hildebrandt¹

2021

Preprint

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Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimises for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problem of feedback loops, filter bubbles and echo chambers. In this article I will discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the GDPR and the proposed AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR) and by requiring documented evidence of the robustness, resilience, reliability and the responsible design and use of high risk recommender systems (AI Act).

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Section: Intermezzo On Counter Profilingmentioning

confidence: 99%

The Issue of Proxies, And why EU law matters for recommender systems

Hildebrandt¹

2021

Preprint

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“…The USEMP architecture, platform, and tools aim to provide instruments that empower users of SNSs to have more informed control over their data and to understand how the data may be used by SNS providers and third parties. DataBait tools enhance users' awareness of personal-data sharing and privacy management (Popescu et al, 2016). For this project, Facebook was chosen as the representative SNS.…”

Section: Databait Toolmentioning

confidence: 99%

“…1. For a detailed overview of the tool, see Popescu et al (2016) Anna Ståhlbröst (PhD, Luleå University of Technology) is a professor of Information Systems at Luleå University of Technology. Her research interests include living labs, user-driven innovation processes, smart cities, domestic IT use, and online privacy.…”

Section: Authors' Notementioning

confidence: 99%

Awareness of Indirect Information Disclosure on Social Network Sites

Padyab

Päivärinta

Ståhlbröst

et al. 2019

Social Media + Society

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This research investigates user awareness and attitudes toward potential inferences of information posted on social network sites (SNSs). The study reports how user attitudes change after exposure to inferences made based upon information they have disclosed on an SNS, namely, on Facebook. To demonstrate this, two sub-studies involving three focus group sessions were conducted with Facebook users. In the first sub-study, the users received a general introduction to information that can be inferred from posts by using a prototypical privacy-enhancement tool called DataBait. Then, the second sub-study allowed the users to witness the potential inferences of their own Facebook photos and posts by using the DataBait tool. Next, qualitative content analysis was conducted to analyze the results, and these showed that the participants' attitudes toward privacy on SNSs changed from affective to cognitive when they became aware of potential inferences from actual information posted on their own Facebook accounts. The results imply that end users require more cognitive awareness regarding their genres of disclosure and the effect of their disclosures on their privacy. Moreover, as privacy awareness is contextual, there is a need for more research and development of online tools that will allow users to manage and educate themselves.

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“…A Third Party TET requires the user to entrust a third party with the user's personal data for providing transparency services. An example for a Third Party TET is the DataBait tool by the EU project USEMP [20], which derives guesses and predictions about the user's personality by analyzing the user's social media and browser data with machine learning software. User side (or user controlled) TETs store the user's personal information to be made transparent locally on the user's device under the user's control.…”

Section: Transparency Enhancing Toolsmentioning

confidence: 99%

“…16,17), and the right to data portability (Art. 20). The right to data portability is aiming at increasing user choices of online services and allows users to request all their data from a data controller that in turn has to provide the users with the data in a structured, commonly-used machine readable format which can then be transmitted to any other controllers.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Exports of Personal Data by Exercising the Right of Data Portability in the Data Track - Are People Ready for This?

Karegar

Pulls

Fischer‐Hübner

2016

IFIP Advances in Information and Communication Technology

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A transparency enhancing tool called Data Track has been developed at Karlstad University. The latest stand-alone version of the tool allows users to visualize their data exports. For analyzing the users' perceptions of the Data Track in regard to transparency features and the concepts of data export and data portability, we have conducted a qualitative user study. We observed that although users had rather little interest in the visualization of derived data activities revealed in the Google location file, they were interested in other kinds of derived data like usage patterns for different service providers. Also, as earlier user studies revealed, we again confirmed that it is confusing for users to differentiate between locally and remotely stored and controlled data. Finally, in spite of being concerned about the security of the data exported to their machines, for exercising data portability rights pursuant to the General Data Protection Regulation, most participants would prefer to first export and edit the data before uploading it to another service provider and would appreciate using a tool such as the Data Track for helping them in this context.

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Increasing Transparency and Privacy for Online Social Network Users – USEMP Value Model, Scoring Framework and Legal

Cited by 10 publications

References 6 publications

The Issue of Proxies, And why EU law matters for recommender systems

The Issue of Proxies, And why EU law matters for recommender systems

Awareness of Indirect Information Disclosure on Social Network Sites

Visualizing Exports of Personal Data by Exercising the Right of Data Portability in the Data Track - Are People Ready for This?

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