DIPA2: An Image Dataset with Cross-cultural Privacy Perception Annotations
Open accessAbstract
The world today is increasingly visual. Many of the most popular online social networking services are largely powered by images, making image privacy protection a critical research topic in the fields of ubiquitous computing, usable security, and human-computer interaction (HCI). One topical issue is understanding privacy-threatening content in images that are shared online. This dataset article introduces DIPA2, an open-sourced image dataset that offers object-level annotations with high-level reasoning properties to show perceptions of privacy among different cultures. DIPA2 provides 5,897 annotations describing perceived privacy risks of 3,347 objects in 1,304 images. The annotations contain the type of the object and four additional privacy metrics: 1) information type indicating what kind of information may leak if the image containing the object is shared, 2) a 7-point Likert item estimating the perceived severity of privacy leakages, and 3) intended recipient scopes when annotators assume they are either image owners or allowing others to repost the image. Our dataset contains unique data from two cultures: We recruited annotators from both Japan and the U.K. to demonstrate the impact of culture on object-level privacy perceptions. In this paper, we first illustrate how we designed and performed the construction of DIPA2, along with data analysis of the collected annotations. Second, we provide two machine-learning baselines to demonstrate how DIPA2 challenges the current image privacy recognition task. DIPA2 facilitates various types of research on image privacy, including machine learning methods inferring privacy threats in complex scenarios, quantitative analysis of cultural influences on privacy preferences, understanding of image sharing behaviors, and promotion of cyber hygiene for general user populations.
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Xu et al. found annotators from an Eastern country like Japan paid less attention to exposing their individual preferences.
“For example, Xu et al. [36] found annotators from an Eastern country like Japan paid less attention to exposing their individual preferences.”
From Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory (2025) · cites this paper as [36]
The privacy risks and preferences may be subject to culture nuances.
“The privacy risks and preferences may subject to culture nuances, as reflected in the prior work [36].”
From Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory (2025) · cites this paper as [36]
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Zhang, S., Zang, Q., Hu, Y. '., Du, J., Wang, X., Kong, Y., Fu, X., Nanayakkara, S. C., Yi, X., & Li, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790288
Zhang, S., Wang, L., Li, S., Wu, Y., Chuai, Y., Chen, L., Yi, X., & Li, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790501
Personalized Image Privacy Advisors via Federated Daisy-Chaining
Banerjee, S., Subramaniam, V., Roy, D., Subbaraju, V., & Bhuyan, M. H. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00275
- Authors
- Xu, A., Zhou, Z., Miyazaki, K., Yoshikawa, R., Hosio, S., & Yatani, K.
- Affiliations
- The University of Tokyo (JP)
- University of Oulu (FI)
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2023-12-19
- Volume
- 7
- Issue
- 4
- Pages
- 1-30
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W4390798746
- Cited by
- 15
- References
- 72
- Citations by year
- 2026: 6 · 2025: 9
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- image privacy, perceived privacy risk, online social networking, cross-cultural comparison, cyber hygiene
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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