"I'm not convinced that they don't collect more than is necessary": User-Controlled Data Minimization Design in Search Engines

Authors: 

Tanusree Sharma, University of Illinois at Urbana Champaign; Lin Kyi, Max Planck Institute for Security and Privacy; Yang Wang, University of Illinois at Urbana-Champaign; Asia J. Biega, Max Planck Institute for Security and Privacy

Abstract: 

Data minimization is a legal and privacy-by-design principle mandating that online services collect only data that is necessary for pre-specified purposes. While the principle has thus far mostly been interpreted from a system-centered perspective, there is a lack of understanding about how data minimization could be designed from a user-centered perspective, and in particular, what factors might influence user decision-making with regard to the necessity of data for different processing purposes. To address this gap, in this paper, we gain a deeper understanding of users' design expectations and decision-making processes related to data minimization, focusing on a case study of search engines. We also elicit expert evaluations of the feasibility of user-generated design ideas. We conducted interviews with 25 end users and 10 experts from the EU and UK to provide concrete design recommendations for data minimization that incorporate user needs, concerns, and preferences. Our study (i) surfaces how users reason about the necessity of data in the context of search result quality, and (ii) examines the impact of several factors on user decision-making about data processing, including specific types of search data, or the volume and recency of data. Most participants emphasized the particular importance of data minimization in the context of sensitive searches, such as political, financial, or health-related search queries. In a think-aloud conceptual design session, participants recommended search profile customization as a solution for retaining data they considered necessary, as well as alert systems that would inform users to minimize data in instances of excessive collection. We propose actionable design features that could provide users with greater agency over their data through user-controlled data minimization, combined with relevant implementation insights from experts.

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