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.

In the era of digital communities, a massive volume of data is created from people's online activities on a daily basis. Such data is sometimes shared with third-parties for commercial benefits, which has caused people's concerns about privacy disclosure. Privacy preserving technologies have been developed to protect people's sensitive information in data publishing. However, due to the availability of data from other sources, e.g., blogging, it is still possible to de-anonymize users even from anonymized data sets. This paper presents the design and implementation of an Interactive De-Anonymization Learning system-IDEAL. The system can help students learn about de-anonymization through engaging hands-on activities, such as tuning different parameters to evaluate their impact on the accuracy of de-anonymization, and observing the affect of data anonymization on de-anonymization. A pilot lab session to evaluate the system was conducted among thirty-five students at Prairie View A&M University and the feedback was very positive.

Online social networks (OSNs) are providing a variety of applications for human users to interact with families, friends, and even strangers. One such application, the friend search engine, allows the general public to query individual users' friend lists and has been gaining popularity recently. However, without proper design, this application may mistakenly disclose users' private relationship information. Our previous work has proposed a privacy preservation solution that can effectively boost OSNs' sociability while protecting users' friendship privacy against attacks launched by individual malicious requestors. In this paper, we propose an advanced collusion attack, where a victim user's friendship privacy can be compromised through a series of carefully designed queries coordinately launched by multiple malicious requestors. The effect of the proposed collusion attack is validated through synthetic and real-world social network data sets. The in-depth research on the advanced collusion attacks will help us design a more robust and secure friend search engine on OSNs in the near future.

Online Social Networks (OSNs) are providing a variety of applications for human users to interact with families, friends and even strangers. One of such applications, friend search engine, which allows the general public to query individual users' friend list, is gaining popularity recently. However, without proper design, this application may disclose users' private relationship information. In this paper, we propose an advanced collusion attack, where a victim user's friendship privacy setting can be compromised through a carefully designed query sequence coordinately launched by multiple malicious requstors. The effect of the proposed collusion attack is validated through synthetic social network data sets. In addition, the analysis of such advanced collusion attacks will also benefit the future design of more robust privacy preserving friend search engine in OSNs.

The availability and diffusion of wireless sensor nodes, personal communication devices (e.g., smartphones), as well as application-specific devices (e.g., surveillance cameras) has changed the typical sensing application scenarios where data are collected from the environment for the purpose of monitoring a phenomenon and detecting events. The combination of highly heterogeneous devices, in terms of sensing, processing, and communication capabilities, has become a key feature to collaborative, distributed, and multimodal sensing applications. However, the heterogeneity of devices also raises a number of challenges for the application developers. In this paper, we present a general software framework for heterogeneous and multimedia wireless sensor networks. The framework abstracts from the individual sensing devices and platforms, and enables collaborative and distributed sensing applications. We present a reference application scenario represented by Assisted Living Environments (ALEs).We show the potential of our proposed framework by a preliminary testbed implementation consisting in a multimodal application for fall detection of elderly people.