In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss.
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Sharing Social Network Data: Differentially Private Estimation of Exponential Family Random-Graph Models
Motivated by a real life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyse synthetic graphs to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case-study using a version of the Enron e-mail corpus data set demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analysing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under ε-edge differential privacy and then use likelihood-based inference for missing data and Markov chain Monte Carlo techniques to fit exponential family random-graph models to the generated synthetic networks.
more » « less- NSF-PAR ID:
- 10398875
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Volume:
- 66
- Issue:
- 3
- ISSN:
- 0035-9254
- Page Range / eLocation ID:
- p. 481-500
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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