Mário J. Silva
Pedro Rijo
ABSTRACT
We address the publication of a large academic information dataset addressing privacy issues. We evaluate anonymization techniques achieving the intended protection, while retaining the utility of the anonymized data. The released data could help infer behaviors and subsequently find solutions for daily planning activities, such as cafeteria attendance, cleaning schedules or student performance, or study interaction patterns among an academic population. However, the nature of the academic data is such that many implicit social interaction networks can be derived from the anonymized datasets, raising the need for researching how anonymity can be assessed in this setting.
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Digital Library
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Index Terms
- Evaluating the Impact of Anonymization on Large Interaction Network Datasets
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