We are honored to be invited for two talks at this year’s Forum Privatheit conference to present our ongoing research. 

Abstracts

Advocating Patient Values in Medical Data Donation through Participation: Using a Method for Unfolding Values Systematically to Inform Healthcare Data Practices– Peter Sörries, David Leimstädtner & Claudia Müller-Birn

The GDPR is intended to limit the collection, processing, and sharing of personal data, but it is becoming clear that more than these measures are needed. Empowering individuals to control their data remains a challenge. Existing consent processes do not adequately support data sovereignty. Companies use manipulative designs to obtain consent that is not in the user's best interest. The question is how to improve data sovereignty, especially in the area of health data. National initiatives call for developing health research data platforms to enable data sharing.

We developed a methodology to identify patient values related to consent processes and translate them into technology recommendations. We facilitated participatory workshops to elicit patient values, needs, and concerns systematically. The methodology was used with patient advocates from vulnerable groups and patients with psychosomatic conditions.

The results were used to formulate design recommendations for consent processes for donating medical health data from the patient's perspective. These design recommendations enable patients to make informed decisions consistent with their values and needs. It is important to adopt patient-centered approaches to health data collection.

Our approach has been used in other contexts, and we hope it will be further validated. We aim to inspire researchers, practitioners, and policymakers to use participatory approaches in developing new privacy technologies.


Sharing Data Fairly with Differential Privacy: Harnessing the Power of Privacy Guarantees for Society– Daniel Franzen & Claudia Müller-Birn

Differential Privacy (DP) is a powerful tool for protecting privacy in data sharing. It offers precise control over the trade-off between data accuracy and privacy protection, allowing data collectors to adjust the privacy parameter ε for exact guarantees. However, the potential of DP is often underutilized due to several challenges.

One key challenge is the lack of informed decision-making by data donors. Without understanding the implications of the privacy parameter ε, they cannot make meaningful choices. Setting ε too high renders privacy protection meaningless, while setting it too low may limit data accuracy. Clear communication and guidance are essential to help data donors navigate this decision.

Another challenge is the misapplication of DP, where it is used as a mere token gesture without providing adequate privacy protection. Data collectors may choose high values of ε to maximize data accuracy, neglecting the intended privacy guarantees. This undermines the purpose of DP and fails to address privacy concerns effectively.

Furthermore, communicating the concepts of DP and privacy risks is complex. The mathematical definition of DP and the abstract nature of privacy risks make conveying their significance to stakeholders challenging. Effective risk communication requires considering the specific context and tailoring the message accordingly.

To unlock the meaningful use of DP, addressing these challenges is crucial. Providing clear and accessible guidance to data donors about the privacy parameter ε is necessary for informed decision-making. Data collectors must understand and respect the intended privacy guarantees of DP, avoiding its misuse as a superficial measure. Improved communication strategies should be developed to effectively convey the concepts of DP and privacy risks to stakeholders, considering the specific context in which they operate. By tackling these issues, DP can be harnessed to its full potential in safeguarding privacy while enabling valuable data sharing.