Privacy-preserving incremental data dissemination

Ji-Won Byun, Tiancheng Li, Elisa Bertino, Ninghui Li, Yonglak Sohn
2009 Journal of Computer Security  
Although the k-anonymity and -diversity models have led to a number of valuable privacy-protecting techniques and algorithms, the existing solutions are currently limited to static data release. That is, it is assumed that a complete dataset is available at the time of data release. This assumption implies a significant shortcoming, as in many applications data collection is rather a continual process. Moreover, the assumption entails "one-time" data dissemination; thus, it does not adequately
more » ... ddress today's strong demand for immediate and up-to-date information. In this paper, we consider incremental data dissemination, where a dataset is continuously incremented with new data. The key issue here is that the same data may be anonymized and published multiple times, each of the time in a different form. Thus, static anonymization (i.e., anonymization which does not consider previously released data) may enable various types of inference. In this paper, we identify such inference issues and discuss some prevention methods. 1 We discuss more robust -diversity requirements in Section 2.
doi:10.3233/jcs-2009-0316 fatcat:tn7iwmozk5b2hhou5pz5yrktjq