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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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Local View Based Connectivity Search in Online Social Networks
One of the challenges in social media research is that, often times, researchers or third parties could not obtain the massive of data collected by a limited number of “big brothers” (e.g., Facebook and Google). In this paper, we shed light on leveraging social network topological properties and local information to effectively conduct search in Online Social Networks (OSN). The problem we focus on is to discover the reachability of a group of target people in an OSN, particularly from the perspective of a third-party analyst who does not have full access to the OSN. We developed effective and efficient detection techniques which demand only a small number of queries to discover people's connections (e.g. friendship) in the OSN. After conducting experiments on real-world data sets, we found that our proposed techniques perform as well as the centralized detection algorithm, which assumes the availability of the global information in the OSN.
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- Award ID(s):
- 1712496
- PAR ID:
- 10438711
- Date Published:
- Journal Name:
- IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops
- Page Range / eLocation ID:
- 372 to 377
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFCOMW.2019.8845271
