Sheila Zingg

Sheila Zingg, Daniele Lain, Yoshimichi Nakatsuka et al.

The European Union will introduce the EUDI Wallet by late 2026, which allows users to hold digital credentials (i.e., representations of physical official identity documents) on their devices. This will allow users to securely and privately disclose identity attributes to websites. Although such a system has many benefits, it also introduces risks caused by poor credential disclosure decisions. In this paper, we (i) conduct a large-scale survey on credential disclosure with users and experts and (ii) evaluate the effectiveness and feasibility of our Credential Assistant that displays expert recommendations and user opinions. Our results show that users are likely to overshare (e.g., ~20% of users disclosed their official ID to news websites). This indicates that users struggle to protect their privacy, which will impact the usability of the EUDI Wallet and lead to privacy violations, identity theft, and other abuses of leaked credentials. Finally, we show that our Credential Assistant significantly reduces users' credential disclosure mistakes from ~15% to ~7%. However, it does not fully eliminate poor credential disclosure decisions, indicating that stronger interventions may be necessary, especially for sensitive attributes.

David M. Sommer, Lukas Abfalterer, Sheila Zingg et al.

Differentially private (DP) mechanisms face the challenge of providing accurate results while protecting their inputs: the privacy-utility trade-off. A simple but powerful technique for DP adds noise to sensitivity-bounded query outputs to blur the exact query output: additive mechanisms. While a vast body of work considers infinitely wide noise distributions, some applications (e.g., real-time operating systems) require hard bounds on the deviations from the real query, and only limited work on such mechanisms exist. An additive mechanism with truncated noise (i.e., with bounded range) can offer such hard bounds. We introduce a gradient-descent-based tool to learn truncated noise for additive mechanisms with strong utility bounds while simultaneously optimizing for differential privacy under sequential composition, i.e., scenarios where multiple noisy queries on the same data are revealed. Our method can learn discrete noise patterns and not only hyper-parameters of a predefined probability distribution. For sensitivity bounded mechanisms, we show that it is sufficient to consider symmetric and that\new{, for from the mean monotonically falling noise,} ensuring privacy for a pair of representative query outputs guarantees privacy for all pairs of inputs (that differ in one element). We find that the utility-privacy trade-off curves of our generated noise are remarkably close to truncated Gaussians and even replicate their shape for $l_2$ utility-loss. For a low number of compositions, we also improved DP-SGD (sub-sampling). Moreover, we extend Moments Accountant to truncated distributions, allowing to incorporate mechanism output events with varying input-dependent zero occurrence probability.