IDEAL: An Interactive De-Anonymization Learning System

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.