Privacy-by-Design in Big Data Analytics and Social Mining
| Title | Privacy-by-Design in Big Data Analytics and Social Mining |
| Publication Type | Journal Article |
| Year of Publication | 2014 |
| Authors | Monreale, A, Rinzivillo, S, Pratesi, F, Giannotti, F, Pedreschi, D |
| Refereed Designation | Unknown |
| Journal | EPJ Data Science |
| Volume | 10 |
| Abstract | Privacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start. |
| DOI | 10.1140/epjds/s13688-014-0010-4 |
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